{ "schema": "ml-prep/search@2", "items": [ { "id": "mit-6-100l-finger-06", "title": "Bisection search finger exercise", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "binary-search", "loops", "conditionals" ], "priority": "foundation", "difficulty": "easy", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "integer-arithmetic" ], "summary": "A small official exercise that separates binary-search reasoning from library use.", "path": "item/mit-6-100l-finger-06/", "url": "https://mlprep.iwase.dev/item/mit-6-100l-finger-06/" }, { "id": "mit-6-100l-finger-11", "title": "List mutation and sorting finger exercise", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "python", "topic_title": "Python", "skills": [ "python-sequence-semantics", "mutability" ], "priority": "foundation", "difficulty": "easy", "origin": "external", "estimated_minutes": 8, "prerequisites": [ "lists" ], "summary": "Directly tests in-place mutation and ordering semantics.", "path": "item/mit-6-100l-finger-11/", "url": "https://mlprep.iwase.dev/item/mit-6-100l-finger-11/" }, { "id": "mit-6-100l-finger-14", "title": "Dictionary selection finger exercise", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "dictionaries", "sets", "hash-map" ], "priority": "foundation", "difficulty": "easy", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "dictionaries" ], "summary": "Uses dictionary iteration, filtering, and deterministic output ordering.", "path": "item/mit-6-100l-finger-14/", "url": "https://mlprep.iwase.dev/item/mit-6-100l-finger-14/" }, { "id": "mit-6-100l-finger-15", "title": "Recursive power finger exercise", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "recursion", "topic_title": "Recursion", "skills": [ "base-case", "recursive-trace", "recursive-implementation" ], "priority": "foundation", "difficulty": "easy", "origin": "external", "estimated_minutes": 7, "prerequisites": [ "functions" ], "summary": "A minimal official check of base and recursive cases.", "path": "item/mit-6-100l-finger-15/", "url": "https://mlprep.iwase.dev/item/mit-6-100l-finger-15/" }, { "id": "mit-6-100l-finger-16", "title": "Recursive flattening finger exercise", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "recursion", "topic_title": "Recursion", "skills": [ "base-case", "recursive-implementation", "sequence-data-structures" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "recursion", "lists" ], "summary": "Adds recursive structural decomposition beyond a scalar recurrence.", "path": "item/mit-6-100l-finger-16/", "url": "https://mlprep.iwase.dev/item/mit-6-100l-finger-16/" }, { "id": "mit-6-100l-finger-23", "title": "Asymptotic complexity finger exercise", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "python", "topic_title": "Python", "skills": [ "complexity", "loops" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "loops" ], "summary": "Tests complexity from actual Python operations rather than memorized algorithm names.", "path": "item/mit-6-100l-finger-23/", "url": "https://mlprep.iwase.dev/item/mit-6-100l-finger-23/" }, { "id": "google-mlcc-linear-regression-quiz", "title": "Linear regression knowledge check", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "regression", "topic_title": "Regression", "skills": [ "linear-regression", "squared-loss", "gradient-update" ], "priority": "foundation", "difficulty": "easy", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "algebra" ], "summary": "An authoritative interactive check of the regression objective and training loop.", "path": "item/google-mlcc-linear-regression-quiz/", "url": "https://mlprep.iwase.dev/item/google-mlcc-linear-regression-quiz/" }, { "id": "google-mlcc-logistic-quiz", "title": "Logistic regression knowledge check", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "classification", "topic_title": "Classification", "skills": [ "logistic-regression", "cross-entropy", "optimization-regularization" ], "priority": "foundation", "difficulty": "easy", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "probability" ], "summary": "Checks probability interpretation, loss, and regularization on the official Google course.", "path": "item/google-mlcc-logistic-quiz/", "url": "https://mlprep.iwase.dev/item/google-mlcc-logistic-quiz/" }, { "id": "google-mlcc-classification-quiz", "title": "Classification metrics knowledge check", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "classification", "topic_title": "Classification", "skills": [ "confusion-matrix", "accuracy", "precision-recall", "decision-boundary", "classification-thresholds" ], "priority": "core", "difficulty": "easy", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "fractions" ], "summary": "Authoritative questions on threshold-dependent classification metrics.", "path": "item/google-mlcc-classification-quiz/", "url": "https://mlprep.iwase.dev/item/google-mlcc-classification-quiz/" }, { "id": "google-mlcc-overfitting-quiz", "title": "Generalization and overfitting knowledge check", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "theory", "topic_title": "Theory", "skills": [ "generalization", "overfitting", "train-validation-test", "model-complexity", "optimization-regularization" ], "priority": "core", "difficulty": "easy", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "training-loss" ], "summary": "Covers the evaluation distinctions most often confused under time pressure.", "path": "item/google-mlcc-overfitting-quiz/", "url": "https://mlprep.iwase.dev/item/google-mlcc-overfitting-quiz/" }, { "id": "google-mlcc-neural-network-quiz", "title": "Neural-network knowledge check", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "neural-networks", "topic_title": "Neural Networks", "skills": [ "forward-pass", "activations", "backpropagation", "neural-network-losses" ], "priority": "core", "difficulty": "easy", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "linear-models" ], "summary": "A concise official check of architecture, activations, and training.", "path": "item/google-mlcc-neural-network-quiz/", "url": "https://mlprep.iwase.dev/item/google-mlcc-neural-network-quiz/" }, { "id": "leetcode-20-valid-parentheses", "title": "Valid Parentheses", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "stack", "conditionals" ], "priority": "foundation", "difficulty": "easy", "origin": "external", "estimated_minutes": 12, "prerequisites": [ "strings" ], "summary": "A standard stack invariant problem with immediate judge feedback on malformed nesting and edge cases.", "path": "item/leetcode-20-valid-parentheses/", "url": "https://mlprep.iwase.dev/item/leetcode-20-valid-parentheses/" }, { "id": "leetcode-215-kth-largest", "title": "Kth Largest Element in an Array", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "heap", "complexity" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 20, "prerequisites": [ "arrays", "priority-queues" ], "summary": "Requires selecting an appropriate heap or selection strategy under an explicit complexity target and provides judge feedback.", "path": "item/leetcode-215-kth-largest/", "url": "https://mlprep.iwase.dev/item/leetcode-215-kth-largest/" }, { "id": "leetcode-704-binary-search", "title": "Binary Search", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "binary-search", "conditionals", "loops" ], "priority": "foundation", "difficulty": "easy", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "sorted-arrays" ], "summary": "Tests interval invariants and termination on a canonical sorted-array search with judge feedback.", "path": "item/leetcode-704-binary-search/", "url": "https://mlprep.iwase.dev/item/leetcode-704-binary-search/" }, { "id": "leetcode-752-open-the-lock", "title": "Open the Lock", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "queue", "bfs", "dictionaries", "sets" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 25, "prerequisites": [ "graphs", "shortest-path" ], "summary": "Models an implicit state graph and requires BFS with visited-state handling; submissions receive judge feedback.", "path": "item/leetcode-752-open-the-lock/", "url": "https://mlprep.iwase.dev/item/leetcode-752-open-the-lock/" }, { "id": "leetcode-200-number-of-islands", "title": "Number of Islands", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "dfs", "bfs", "queue", "recursive-implementation" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 25, "prerequisites": [ "grids", "graph-traversal" ], "summary": "Uses recursive or iterative flood fill to test traversal state, base cases, and component counting with judge feedback.", "path": "item/leetcode-200-number-of-islands/", "url": "https://mlprep.iwase.dev/item/leetcode-200-number-of-islands/" }, { "id": "mit-18-02sc-f2010-exam2-q1", "title": "Gradient, tangent plane, linearization, and directional derivative", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "gradient", "directional-derivative" ], "priority": "core", "difficulty": "easy", "origin": "external", "estimated_minutes": 12, "prerequisites": [ "partial-derivatives" ], "summary": "A fast diagnostic of the multivariable differential concepts most likely to transfer to ML.", "path": "item/mit-18-02sc-f2010-exam2-q1/", "url": "https://mlprep.iwase.dev/item/mit-18-02sc-f2010-exam2-q1/" }, { "id": "mit-18-02sc-f2010-exam2-q3", "title": "Critical points and constrained domain boundaries", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "calculus-stationary-points", "unconstrained-optimization" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 14, "prerequisites": [ "partial-derivatives", "quadratic-functions" ], "summary": "Separates local critical-point classification from optimization over a constrained region.", "path": "item/mit-18-02sc-f2010-exam2-q3/", "url": "https://mlprep.iwase.dev/item/mit-18-02sc-f2010-exam2-q3/" }, { "id": "mit-18-02sc-f2010-exam2-q4", "title": "Multivariable chain rule under a change of variables", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "multivariable-chain-rule", "partial-derivatives" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "partial-derivatives" ], "summary": "Tests symbolic chain-rule bookkeeping that directly supports backpropagation and reparameterization.", "path": "item/mit-18-02sc-f2010-exam2-q4/", "url": "https://mlprep.iwase.dev/item/mit-18-02sc-f2010-exam2-q4/" }, { "id": "mit-18-02sc-f2010-exam2-q5", "title": "Lagrange multiplier equations and tangent geometry", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "constrained-optimization" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "gradient", "implicit-surfaces" ], "summary": "Focuses on setting up constrained optimization and interpreting the shared normal direction.", "path": "item/mit-18-02sc-f2010-exam2-q5/", "url": "https://mlprep.iwase.dev/item/mit-18-02sc-f2010-exam2-q5/" }, { "id": "mit-18-02sc-f2010-exam2-q6", "title": "Total differentials and implicit differentiation", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "implicit-differentiation", "linear-systems" ], "priority": "supplementary", "difficulty": "medium", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "partial-derivatives" ], "summary": "Checks whether local differential constraints can be combined and solved without treating variables as independent.", "path": "item/mit-18-02sc-f2010-exam2-q6/", "url": "https://mlprep.iwase.dev/item/mit-18-02sc-f2010-exam2-q6/" }, { "id": "mit-18-05-s2022-exam1-q1b", "title": "Events, unions, and conditional probability", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "event-algebra", "inclusion-exclusion", "conditional-probability" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 6, "prerequisites": [ "basic-algebra" ], "summary": "A compact check of whether event notation and conditioning are operational rather than merely familiar.", "path": "item/mit-18-05-s2022-exam1-q1b/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-exam1-q1b/" }, { "id": "mit-18-05-s2022-exam1-q3", "title": "Joint tables, Bayes rule, and independence", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "bayes-rule", "probabilistic-independence" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 14, "prerequisites": [ "basic-algebra" ], "summary": "Uses one finite model to test joint and marginal probabilities, reverse conditioning, and independence.", "path": "item/mit-18-05-s2022-exam1-q3/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-exam1-q3/" }, { "id": "mit-18-05-s2022-exam1-q4", "title": "Diagnostic testing with base rates", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "bayes-rule", "conditional-probability" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 8, "prerequisites": [ "basic-algebra" ], "summary": "A short, discriminative Bayes calculation whose interpretation transfers directly to ML evaluation.", "path": "item/mit-18-05-s2022-exam1-q4/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-exam1-q4/" }, { "id": "mit-18-05-s2022-exam1-q5", "title": "Joint density, moments, covariance, and correlation", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "joint-distributions", "marginal-distributions", "conditional-distributions", "probabilistic-independence", "expectation", "variance" ], "priority": "core", "difficulty": "hard", "origin": "external", "estimated_minutes": 24, "prerequisites": [ "basic-algebra" ], "summary": "Integrates continuous probability, moments, dependence, and a geometric integration region in one coherent problem.", "path": "item/mit-18-05-s2022-exam1-q5/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-exam1-q5/" }, { "id": "mit-18-05-s2022-exam1-q6", "title": "Central limit theorem for an aggregate", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "central-limit-theorem", "sums-of-random-variables" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "basic-algebra" ], "summary": "Tests whether the learner can propagate mean and variance and standardize an aggregate.", "path": "item/mit-18-05-s2022-exam1-q6/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-exam1-q6/" }, { "id": "mit-18-05-s2022-exam2-i2", "title": "Type I error, Type II error, and power", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "null-alternative", "type-i-error", "type-ii-error", "statistical-power" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 5, "prerequisites": [ "basic-algebra" ], "summary": "A rapid conceptual diagnostic that catches common confusion among significance, power, and error rates.", "path": "item/mit-18-05-s2022-exam2-i2/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-exam2-i2/" }, { "id": "mit-18-05-s2022-exam2-ii1", "title": "Maximum likelihood estimation from a density", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "likelihood", "maximum-likelihood" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 12, "prerequisites": [ "basic-algebra" ], "summary": "Requires constructing and differentiating a likelihood rather than quoting an estimator.", "path": "item/mit-18-05-s2022-exam2-ii1/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-exam2-ii1/" }, { "id": "mit-18-05-s2022-exam2-ii2", "title": "Bayesian posterior density and normalization", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "bayesian-updating" ], "priority": "core", "difficulty": "hard", "origin": "external", "estimated_minutes": 15, "prerequisites": [ "basic-algebra" ], "summary": "Tests likelihood construction, prior multiplication, and posterior normalization in a finite-outcome model.", "path": "item/mit-18-05-s2022-exam2-ii2/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-exam2-ii2/" }, { "id": "mit-18-05-s2022-exam2-ii4", "title": "One-sided test, critical value, and p-value", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "null-alternative", "p-values", "critical-regions" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 12, "prerequisites": [ "basic-algebra" ], "summary": "Connects a rejection region, significance level, observed statistic, and p-value without relying on one mnemonic.", "path": "item/mit-18-05-s2022-exam2-ii4/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-exam2-ii4/" }, { "id": "mit-18-05-s2022-exam2-ii5", "title": "Chi-square test for a contingency table", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "goodness-of-fit", "chi-square" ], "priority": "core", "difficulty": "hard", "origin": "external", "estimated_minutes": 18, "prerequisites": [ "basic-algebra" ], "summary": "Checks end-to-end statistical reasoning from hypotheses through expected counts to a justified decision.", "path": "item/mit-18-05-s2022-exam2-ii5/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-exam2-ii5/" }, { "id": "mit-18-06-s2010-exam1-q2", "title": "Nullspace, column space, pivots, and block structure", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "matrix-operations", "linear-systems", "row-reduction", "subspaces", "rank", "rank-nullity" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 14, "prerequisites": [ "linear-systems", "reduced-row-echelon-form" ], "summary": "Distinguishes procedural row reduction from geometric understanding of the four fundamental subspaces.", "path": "item/mit-18-06-s2010-exam1-q2/", "url": "https://mlprep.iwase.dev/item/mit-18-06-s2010-exam1-q2/" }, { "id": "mit-18-06-s2010-exam2-q1", "title": "Projection matrix as geometry and spectrum", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "projection", "eigenvalues", "eigenvectors" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 15, "prerequisites": [ "orthogonality", "outer-products" ], "summary": "Connects a projection formula to its subspaces and eigenstructure, a high-value ML pattern.", "path": "item/mit-18-06-s2010-exam2-q1/", "url": "https://mlprep.iwase.dev/item/mit-18-06-s2010-exam2-q1/" }, { "id": "mit-18-06-s2010-exam2-q2", "title": "Least squares, QR, and orthonormal projection", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "least-squares", "normal-equations", "qr", "projection", "orthogonality" ], "priority": "core", "difficulty": "hard", "origin": "external", "estimated_minutes": 18, "prerequisites": [ "orthogonality", "matrix-inverse" ], "summary": "Tests the derivation and interpretation of least squares rather than formula recall alone.", "path": "item/mit-18-06-s2010-exam2-q2/", "url": "https://mlprep.iwase.dev/item/mit-18-06-s2010-exam2-q2/" }, { "id": "mit-18-06-s2010-exam3-q1", "title": "Symmetry, orthogonality, and eigenspaces", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "symmetric-matrices", "eigenvalues", "eigenvectors", "diagonalization" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 16, "prerequisites": [ "outer-products", "orthogonal-complements" ], "summary": "Uses a structured matrix to test proof, geometry, multiplicity, and trace in one compact question.", "path": "item/mit-18-06-s2010-exam3-q1/", "url": "https://mlprep.iwase.dev/item/mit-18-06-s2010-exam3-q1/" }, { "id": "mit-18-06-s2010-exam3-q2", "title": "Positive definite and semidefinite transformations", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "positive-definite-semidefinite", "quadratic-forms" ], "priority": "core", "difficulty": "hard", "origin": "external", "estimated_minutes": 14, "prerequisites": [ "eigenvalues", "quadratic-forms" ], "summary": "Exercises three interchangeable tests for positive definiteness and a useful block-matrix argument.", "path": "item/mit-18-06-s2010-exam3-q2/", "url": "https://mlprep.iwase.dev/item/mit-18-06-s2010-exam3-q2/" }, { "id": "mit-18-06-s2010-exam3-q3c", "title": "Singular value decomposition from the Gram matrix", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "svd", "orthogonality" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 10, "prerequisites": [ "eigenvalues", "orthogonality" ], "summary": "A compact check that the learner can construct an SVD from the eigendecomposition of a Gram matrix.", "path": "item/mit-18-06-s2010-exam3-q3c/", "url": "https://mlprep.iwase.dev/item/mit-18-06-s2010-exam3-q3c/" }, { "id": "mit-6-036-s2018-final-q9", "title": "Convolutional representation and detector design", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "convolution", "topic_title": "Convolution", "skills": [ "convolution-operation", "output-shape", "receptive-field" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 18, "prerequisites": [ "matrix-indexing", "relu" ], "summary": "Requires an actual convolution and composes small filters into a deeper detector instead of testing shape recall alone.", "path": "item/mit-6-036-s2018-final-q9/", "url": "https://mlprep.iwase.dev/item/mit-6-036-s2018-final-q9/" }, { "id": "mit-6-036-s2019-final-q4", "title": "CNN shapes, pooling, and backpropagation", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "convolution", "topic_title": "Convolution", "skills": [ "convolution-operation", "output-shape", "convolution-parameter-count", "pooling" ], "priority": "supplementary", "difficulty": "hard", "origin": "external", "estimated_minutes": 25, "prerequisites": [ "backpropagation", "max-pooling", "sgd" ], "summary": "Integrates convolutional shapes, loss choice, differentiation through convolution and pooling, and an SGD debugging decision.", "path": "item/mit-6-036-s2019-final-q4/", "url": "https://mlprep.iwase.dev/item/mit-6-036-s2019-final-q4/" }, { "id": "mit-6-036-s2019-midterm-q5", "title": "Learning as optimization", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "optimization", "topic_title": "Optimization", "skills": [ "objective-functions", "optimization-convexity", "optimization-diagnostics", "optimization-stationary-points" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 18, "prerequisites": [ "derivatives", "gradient-descent", "loss-functions" ], "summary": "Derives an update and distinguishes slow, oscillating, and divergent learning-rate behavior from objective geometry.", "path": "item/mit-6-036-s2019-midterm-q5/", "url": "https://mlprep.iwase.dev/item/mit-6-036-s2019-midterm-q5/" }, { "id": "mit-6-036-s2019-midterm-q4", "title": "Diagnose model evaluation failures", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "theory", "topic_title": "Theory", "skills": [ "bias-variance", "generalization", "overfitting", "train-validation-test", "model-complexity" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 15, "prerequisites": [ "classification-metrics" ], "summary": "Uses observed train and held-out behavior to separate overfitting, insufficient expressivity, data scarcity, and distribution shift.", "path": "item/mit-6-036-s2019-midterm-q4/", "url": "https://mlprep.iwase.dev/item/mit-6-036-s2019-midterm-q4/" }, { "id": "mit-6-100l-finger-05", "title": "Even-indexed characters finger exercise", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "control-flow", "topic_title": "Control Flow", "skills": [ "conditionals", "loops", "strings" ], "priority": "foundation", "difficulty": "easy", "origin": "external", "estimated_minutes": 5, "prerequisites": [ "string-indexing" ], "summary": "Uses a compact string traversal to test index parity, loop state, and output construction.", "path": "item/mit-6-100l-finger-05/", "url": "https://mlprep.iwase.dev/item/mit-6-100l-finger-05/" }, { "id": "mit-6-100l-finger-08", "title": "Compare character sets in two strings", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "control-flow", "topic_title": "Control Flow", "skills": [ "conditionals", "loops", "strings" ], "priority": "core", "difficulty": "easy", "origin": "external", "estimated_minutes": 6, "prerequisites": [ "membership" ], "summary": "Tests bidirectional membership reasoning rather than only one pass over one string.", "path": "item/mit-6-100l-finger-08/", "url": "https://mlprep.iwase.dev/item/mit-6-100l-finger-08/" }, { "id": "mit-6-100l-finger-17", "title": "Circle class and value equality", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "python", "topic_title": "Python", "skills": [ "classes", "value-equality", "python-syntax", "functions" ], "priority": "supplementary", "difficulty": "medium", "origin": "external", "estimated_minutes": 12, "prerequisites": [ "methods", "object-state" ], "summary": "Exercises object state, accessors, mutation, and explicit value equality through a small class contract.", "path": "item/mit-6-100l-finger-17/", "url": "https://mlprep.iwase.dev/item/mit-6-100l-finger-17/" }, { "id": "mit-6-100l-finger-19", "title": "Stack subclass implementation", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "stack", "classes", "sequence-data-structures" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 15, "prerequisites": [ "lists", "methods" ], "summary": "Implements a LIFO abstraction while distinguishing inherited container behavior from stack-specific removal.", "path": "item/mit-6-100l-finger-19/", "url": "https://mlprep.iwase.dev/item/mit-6-100l-finger-19/" }, { "id": "mit-18-01sc-f2010-final-foundations", "title": "Single-variable calculus foundation check", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "limits", "continuity", "single-variable-differentiation", "single-variable-integration", "taylor-approximation" ], "priority": "foundation", "difficulty": "easy", "origin": "external", "estimated_minutes": 24, "prerequisites": [ "polynomials" ], "summary": "Uses official exam questions to check differentiation, a limit, definite integration, and Taylor approximation independently.", "path": "item/mit-18-01sc-f2010-final-foundations/", "url": "https://mlprep.iwase.dev/item/mit-18-01sc-f2010-final-foundations/" }, { "id": "mit-18-05-s2022-pset11-q1", "title": "Confidence intervals with known and unknown variance", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "confidence-intervals", "standard-error", "sampling-distributions" ], "priority": "core", "difficulty": "easy", "origin": "external", "estimated_minutes": 14, "prerequisites": [ "gaussian-distribution" ], "summary": "Contrasts t and z intervals on the same data and extends the calculation to sample-size planning.", "path": "item/mit-18-05-s2022-pset11-q1/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-pset11-q1/" }, { "id": "mit-18-05-s2022-pset6-q2", "title": "Simple linear regression from Gaussian maximum likelihood", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "simple-linear-regression", "maximum-likelihood", "likelihood" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 25, "prerequisites": [ "gaussian-distribution", "partial-derivatives" ], "summary": "Derives a fitted line from an explicit Gaussian error model, linking regression, likelihood, and least squares.", "path": "item/mit-18-05-s2022-pset6-q2/", "url": "https://mlprep.iwase.dev/item/mit-18-05-s2022-pset6-q2/" }, { "id": "original-la-foundations", "title": "Matrix fundamentals under time pressure", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "matrix-operations", "inverse", "determinant", "trace", "matrix-norms" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "matrix-arithmetic" ], "summary": "Original practice item.", "path": "item/original-la-foundations/", "url": "https://mlprep.iwase.dev/item/original-la-foundations/" }, { "id": "original-la-basis-change", "title": "Basis, dimension, and coordinates", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "linear-independence", "basis-dimension", "change-of-basis" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "linear-combinations" ], "summary": "Original practice item.", "path": "item/original-la-basis-change/", "url": "https://mlprep.iwase.dev/item/original-la-basis-change/" }, { "id": "original-la-low-rank", "title": "Low-rank error and explained variance", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "svd", "low-rank-approximation", "pca-via-svd" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "singular-values" ], "summary": "Original practice item.", "path": "item/original-la-low-rank/", "url": "https://mlprep.iwase.dev/item/original-la-low-rank/" }, { "id": "original-calc-jacobian", "title": "Jacobian and chain rule", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "partial-derivatives", "jacobian", "multivariable-chain-rule", "gradient" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 10, "prerequisites": [ "partial-derivatives" ], "summary": "Original practice item.", "path": "item/original-calc-jacobian/", "url": "https://mlprep.iwase.dev/item/original-calc-jacobian/" }, { "id": "original-calc-matrix", "title": "Matrix gradient and Hessian", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "matrix-calculus", "gradient", "hessian", "calculus-convexity" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 10, "prerequisites": [ "matrix-multiplication", "chain-rule" ], "summary": "Original practice item.", "path": "item/original-calc-matrix/", "url": "https://mlprep.iwase.dev/item/original-calc-matrix/" }, { "id": "original-prob-distributions", "title": "Recognize distribution moments", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "discrete-random-variables", "pmf", "cdf", "continuous-random-variables", "pdf", "named-distributions", "sums-of-random-variables", "expectation", "variance" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "random-variables" ], "summary": "Original practice item.", "path": "item/original-prob-distributions/", "url": "https://mlprep.iwase.dev/item/original-prob-distributions/" }, { "id": "original-prob-total-variance", "title": "Conditional expectation and total variance", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "conditional-expectation", "law-total-expectation", "law-total-variance", "expectation", "variance" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "conditional-probability" ], "summary": "Original practice item.", "path": "item/original-prob-total-variance/", "url": "https://mlprep.iwase.dev/item/original-prob-total-variance/" }, { "id": "original-prob-gaussian", "title": "Linear transform of a multivariate Gaussian", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "multivariate-gaussian", "transformations", "covariance", "correlation" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "matrix-multiplication", "gaussian-distribution" ], "summary": "Original practice item.", "path": "item/original-prob-gaussian/", "url": "https://mlprep.iwase.dev/item/original-prob-gaussian/" }, { "id": "original-prob-foundations", "title": "Counting, total probability, and independence", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "counting", "conditional-probability", "probabilistic-independence" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "fractions" ], "summary": "Original practice item.", "path": "item/original-prob-foundations/", "url": "https://mlprep.iwase.dev/item/original-prob-foundations/" }, { "id": "original-prob-lln", "title": "LLN versus CLT", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "law-of-large-numbers", "central-limit-theorem" ], "priority": "core", "difficulty": "easy", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "expectation", "variance" ], "summary": "Original practice item.", "path": "item/original-prob-lln/", "url": "https://mlprep.iwase.dev/item/original-prob-lln/" }, { "id": "original-stats-estimator", "title": "Bias, variance, MSE, and consistency", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "samples", "sample-statistics", "sampling-distributions", "estimator-bias", "estimator-variance", "mean-squared-error", "consistency", "standard-error" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "expectation", "variance" ], "summary": "Original practice item.", "path": "item/original-stats-estimator/", "url": "https://mlprep.iwase.dev/item/original-stats-estimator/" }, { "id": "original-stats-mom-map", "title": "Method of moments and Bayesian updating", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "method-of-moments", "map", "bayesian-updating", "likelihood" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 10, "prerequisites": [ "exponential-distribution", "beta-distribution" ], "summary": "Original practice item.", "path": "item/original-stats-mom-map/", "url": "https://mlprep.iwase.dev/item/original-stats-mom-map/" }, { "id": "original-ml-regression", "title": "Linear regression objective and gradient", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "regression", "topic_title": "Regression", "skills": [ "linear-regression", "squared-loss", "regression-gradient" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "matrix-multiplication", "derivatives" ], "summary": "Original practice item.", "path": "item/original-ml-regression/", "url": "https://mlprep.iwase.dev/item/original-ml-regression/" }, { "id": "original-ml-logistic", "title": "Logistic probability and cross-entropy", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "classification", "topic_title": "Classification", "skills": [ "logistic-regression", "cross-entropy", "decision-boundary", "classification-thresholds" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 6, "prerequisites": [ "logarithms" ], "summary": "Original practice item.", "path": "item/original-ml-logistic/", "url": "https://mlprep.iwase.dev/item/original-ml-logistic/" }, { "id": "original-ml-metrics", "title": "Classification metrics from counts", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "classification", "topic_title": "Classification", "skills": [ "confusion-matrix", "accuracy", "precision-recall" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 6, "prerequisites": [ "fractions" ], "summary": "Original practice item.", "path": "item/original-ml-metrics/", "url": "https://mlprep.iwase.dev/item/original-ml-metrics/" }, { "id": "original-ml-gd", "title": "Gradient descent on a quadratic", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "gradient-descent", "topic_title": "Gradient Descent", "skills": [ "gradient-update", "learning-rate", "convergence" ], "priority": "core", "difficulty": "easy", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "derivatives" ], "summary": "Original practice item.", "path": "item/original-ml-gd/", "url": "https://mlprep.iwase.dev/item/original-ml-gd/" }, { "id": "original-ml-sgd", "title": "Full gradient versus stochastic gradient", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "gradient-descent", "topic_title": "Gradient Descent", "skills": [ "gradient-update", "sgd", "convergence" ], "priority": "core", "difficulty": "easy", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "expectation" ], "summary": "Original practice item.", "path": "item/original-ml-sgd/", "url": "https://mlprep.iwase.dev/item/original-ml-sgd/" }, { "id": "original-ml-pca", "title": "PCA direction and explained variance", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "dimensionality-reduction", "topic_title": "Dimensionality Reduction", "skills": [ "pca-objective", "centering", "covariance-eigendecomposition", "explained-variance" ], "priority": "core", "difficulty": "easy", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "eigenvalues" ], "summary": "Original practice item.", "path": "item/original-ml-pca/", "url": "https://mlprep.iwase.dev/item/original-ml-pca/" }, { "id": "original-ml-kmeans", "title": "One k-means assignment and update", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "unsupervised-learning", "topic_title": "Unsupervised Learning", "skills": [ "k-means", "clustering-objective" ], "priority": "core", "difficulty": "easy", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "squared-distance" ], "summary": "Original practice item.", "path": "item/original-ml-kmeans/", "url": "https://mlprep.iwase.dev/item/original-ml-kmeans/" }, { "id": "original-ml-gmm", "title": "Mixture responsibilities and EM", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "unsupervised-learning", "topic_title": "Unsupervised Learning", "skills": [ "gaussian-mixtures", "expectation-maximization", "clustering-objective" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "bayes-rule" ], "summary": "Original practice item.", "path": "item/original-ml-gmm/", "url": "https://mlprep.iwase.dev/item/original-ml-gmm/" }, { "id": "original-ml-forward-backprop", "title": "Forward pass and backpropagation", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "neural-networks", "topic_title": "Neural Networks", "skills": [ "forward-pass", "activations", "neural-network-chain-rule", "backpropagation", "neural-network-losses" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "chain-rule" ], "summary": "Original practice item.", "path": "item/original-ml-forward-backprop/", "url": "https://mlprep.iwase.dev/item/original-ml-forward-backprop/" }, { "id": "original-ml-network-params", "title": "Dense-network parameter count", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "neural-networks", "topic_title": "Neural Networks", "skills": [ "dense-network-parameter-count" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "matrix-shapes" ], "summary": "Original practice item.", "path": "item/original-ml-network-params/", "url": "https://mlprep.iwase.dev/item/original-ml-network-params/" }, { "id": "original-ml-convolution", "title": "Convolution output shape and parameters", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "convolution", "topic_title": "Convolution", "skills": [ "output-shape", "padding", "stride", "convolution-parameter-count" ], "priority": "core", "difficulty": "easy", "origin": "original", "estimated_minutes": 6, "prerequisites": [ "integer-arithmetic" ], "summary": "Original practice item.", "path": "item/original-ml-convolution/", "url": "https://mlprep.iwase.dev/item/original-ml-convolution/" }, { "id": "original-ml-regularization", "title": "Lasso versus ridge penalties", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "optimization", "topic_title": "Optimization", "skills": [ "optimization-regularization", "objective-functions", "model-complexity", "generalization" ], "priority": "core", "difficulty": "easy", "origin": "original", "estimated_minutes": 6, "prerequisites": [ "norms" ], "summary": "Original practice item.", "path": "item/original-ml-regularization/", "url": "https://mlprep.iwase.dev/item/original-ml-regularization/" }, { "id": "original-ml-finite-class-bound", "title": "Read a finite-class generalization bound", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "theory", "topic_title": "Theory", "skills": [ "generalization", "model-complexity" ], "priority": "stretch", "difficulty": "medium", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "logarithms", "probability" ], "summary": "Original practice item.", "path": "item/original-ml-finite-class-bound/", "url": "https://mlprep.iwase.dev/item/original-ml-finite-class-bound/" }, { "id": "original-py-alias-copy", "title": "Aliasing and shallow copy", "collection": "practice", "area": "programming", "area_title": "Programming", "topic": "python", "topic_title": "Python", "skills": [ "python-sequence-semantics", "mutability", "aliasing", "shallow-copy" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "lists" ], "summary": "Original practice item.", "path": "item/original-py-alias-copy/", "url": "https://mlprep.iwase.dev/item/original-py-alias-copy/" }, { "id": "pml-book1-ex5.1", "title": "Reject option in classifiers", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "classification", "topic_title": "Classification", "skills": [ "decision-boundary", "classification-thresholds" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 12, "prerequisites": [ "conditional-probability", "expected-loss" ], "summary": "Derives a confidence-dependent classification and rejection rule from explicit costs and posterior class probabilities.", "path": "item/pml-book1-ex5.1/", "url": "https://mlprep.iwase.dev/item/pml-book1-ex5.1/" }, { "id": "pml-book1-ex11.2", "title": "Centering and ridge regression", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "regression", "topic_title": "Regression", "skills": [ "linear-regression", "normal-equations", "regression-regularization" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 15, "prerequisites": [ "matrix-multiplication", "centering" ], "summary": "Connects centering, the ridge objective, the intercept, and normal equations in one derivation.", "path": "item/pml-book1-ex11.2/", "url": "https://mlprep.iwase.dev/item/pml-book1-ex11.2/" }, { "id": "pml-book1-ex4.5", "title": "BIC for a two-dimensional discrete distribution", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "theory", "topic_title": "Theory", "skills": [ "generalization", "overfitting", "model-complexity" ], "priority": "core", "difficulty": "medium", "origin": "external", "estimated_minutes": 20, "prerequisites": [ "maximum-likelihood", "logarithms", "cross-validation" ], "summary": "Compares nested models using fit, leave-one-out behavior, parameter count, and a complexity penalty.", "path": "item/pml-book1-ex4.5/", "url": "https://mlprep.iwase.dev/item/pml-book1-ex4.5/" }, { "id": "pml-book1-ex20.3", "title": "Deriving the second principal component", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "dimensionality-reduction", "topic_title": "Dimensionality Reduction", "skills": [ "pca-objective", "covariance-eigendecomposition" ], "priority": "core", "difficulty": "hard", "origin": "external", "estimated_minutes": 18, "prerequisites": [ "eigenvectors", "lagrange-multipliers", "matrix-calculus" ], "summary": "Connects the constrained PCA reconstruction objective to the covariance eigenvector with the second-largest eigenvalue.", "path": "item/pml-book1-ex20.3/", "url": "https://mlprep.iwase.dev/item/pml-book1-ex20.3/" }, { "id": "pml-book1-ex10.1", "title": "Gradient and Hessian of multinomial logistic regression", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "classification", "topic_title": "Classification", "skills": [ "logistic-regression", "cross-entropy" ], "priority": "supplementary", "difficulty": "hard", "origin": "external", "estimated_minutes": 20, "prerequisites": [ "softmax", "chain-rule", "matrix-calculus" ], "summary": "Derives the first- and second-order structure of multiclass logistic loss.", "path": "item/pml-book1-ex10.1/", "url": "https://mlprep.iwase.dev/item/pml-book1-ex10.1/" }, { "id": "pml-book1-ex13.1", "title": "Backpropagation for a multilayer perceptron", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "neural-networks", "topic_title": "Neural Networks", "skills": [ "forward-pass", "activations", "neural-network-chain-rule", "backpropagation", "neural-network-losses" ], "priority": "core", "difficulty": "hard", "origin": "external", "estimated_minutes": 20, "prerequisites": [ "softmax", "cross-entropy", "matrix-calculus", "relu" ], "summary": "Derives layer errors, parameter gradients, and the input gradient for a one-hidden-layer classifier.", "path": "item/pml-book1-ex13.1/", "url": "https://mlprep.iwase.dev/item/pml-book1-ex13.1/" }, { "id": "pml-book1-ex11.4", "title": "Reducing elastic net to lasso", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "optimization", "topic_title": "Optimization", "skills": [ "objective-functions", "optimization-regularization" ], "priority": "supplementary", "difficulty": "hard", "origin": "external", "estimated_minutes": 15, "prerequisites": [ "l1-norm", "l2-norm", "linear-regression" ], "summary": "Transforms a mixed regularization objective into a standard lasso problem on augmented data.", "path": "item/pml-book1-ex11.4/", "url": "https://mlprep.iwase.dev/item/pml-book1-ex11.4/" }, { "id": "pml-book1-ex20.1", "title": "Expectation-maximization for factor analysis", "collection": "practice", "area": "machine-learning", "area_title": "Machine Learning", "topic": "unsupervised-learning", "topic_title": "Unsupervised Learning", "skills": [ "expectation-maximization" ], "priority": "supplementary", "difficulty": "hard", "origin": "external", "estimated_minutes": 30, "prerequisites": [ "multivariate-gaussian", "matrix-calculus", "latent-variables" ], "summary": "Derives E- and M-step updates for a latent Gaussian factor model.", "path": "item/pml-book1-ex20.1/", "url": "https://mlprep.iwase.dev/item/pml-book1-ex20.1/" }, { "id": "harvard-stat110-f2011-sp5-continuous-q1", "title": "Lognormal transformation by the CDF method", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "continuous-random-variables", "pdf", "cdf", "transformations" ], "priority": "supplementary", "difficulty": "medium", "origin": "external", "estimated_minutes": 12, "prerequisites": [ "normal-distribution", "chain-rule" ], "summary": "A compact test of transforming a continuous random variable using a CDF before differentiating for the density.", "path": "item/harvard-stat110-f2011-sp5-continuous-q1/", "url": "https://mlprep.iwase.dev/item/harvard-stat110-f2011-sp5-continuous-q1/" }, { "id": "pml-book1-ex2.9", "title": "Bayes rule and diagnostic evidence", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "total-probability", "bayes-rule", "conditional-probability" ], "priority": "supplementary", "difficulty": "medium", "origin": "external", "estimated_minutes": 12, "prerequisites": [ "conditional-probability" ], "summary": "Connects Bayes rule to evidence interpretation and exposes the common confusion between a likelihood and a posterior.", "path": "item/pml-book1-ex2.9/", "url": "https://mlprep.iwase.dev/item/pml-book1-ex2.9/" }, { "id": "pml-book1-ex3.4", "title": "Linear transformations and quadratic expectations", "collection": "practice", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "covariance", "correlation" ], "priority": "supplementary", "difficulty": "medium", "origin": "external", "estimated_minutes": 15, "prerequisites": [ "expectation", "covariance", "matrix-multiplication" ], "summary": "Exercises the matrix identities behind transformed covariance and expected quadratic forms used throughout machine learning.", "path": "item/pml-book1-ex3.4/", "url": "https://mlprep.iwase.dev/item/pml-book1-ex3.4/" }, { "id": "ml-screen-regression-foundation-gradient", "title": "Squared loss and gradient", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "regression", "topic_title": "Regression", "skills": [ "linear-regression", "squared-loss", "regression-gradient" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 6, "prerequisites": [ "basic-derivatives", "vector-arithmetic" ], "summary": "Original Screening question.", "path": "item/ml-screen-regression-foundation-gradient/", "url": "https://mlprep.iwase.dev/item/ml-screen-regression-foundation-gradient/" }, { "id": "ml-screen-regression-standard-normal-equations", "title": "Solve normal equations", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "regression", "topic_title": "Regression", "skills": [ "linear-regression", "squared-loss", "normal-equations" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "matrix-multiplication", "linear-systems" ], "summary": "Original Screening question.", "path": "item/ml-screen-regression-standard-normal-equations/", "url": "https://mlprep.iwase.dev/item/ml-screen-regression-standard-normal-equations/" }, { "id": "ml-screen-regression-advanced-ridge", "title": "One-dimensional ridge estimate", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "regression", "topic_title": "Regression", "skills": [ "linear-regression", "normal-equations", "regression-regularization" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 10, "prerequisites": [ "derivatives", "least-squares" ], "summary": "Original Screening question.", "path": "item/ml-screen-regression-advanced-ridge/", "url": "https://mlprep.iwase.dev/item/ml-screen-regression-advanced-ridge/" }, { "id": "ml-screen-classification-foundation-logistic", "title": "Logistic probability and loss", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "classification", "topic_title": "Classification", "skills": [ "logistic-regression", "cross-entropy", "decision-boundary", "classification-thresholds" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "logarithms", "sigmoid" ], "summary": "Original Screening question.", "path": "item/ml-screen-classification-foundation-logistic/", "url": "https://mlprep.iwase.dev/item/ml-screen-classification-foundation-logistic/" }, { "id": "ml-screen-classification-standard-metrics", "title": "Metrics from a confusion matrix", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "classification", "topic_title": "Classification", "skills": [ "confusion-matrix", "accuracy", "precision-recall" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 6, "prerequisites": [ "fractions" ], "summary": "Original Screening question.", "path": "item/ml-screen-classification-standard-metrics/", "url": "https://mlprep.iwase.dev/item/ml-screen-classification-standard-metrics/" }, { "id": "ml-screen-classification-advanced-cost-threshold", "title": "Cost-sensitive threshold", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "classification", "topic_title": "Classification", "skills": [ "cross-entropy", "classification-thresholds" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 10, "prerequisites": [ "conditional-risk", "logarithms" ], "summary": "Original Screening question.", "path": "item/ml-screen-classification-advanced-cost-threshold/", "url": "https://mlprep.iwase.dev/item/ml-screen-classification-advanced-cost-threshold/" }, { "id": "ml-screen-gradient-descent-foundation-updates", "title": "Two gradient updates", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "gradient-descent", "topic_title": "Gradient Descent", "skills": [ "gradient-update", "learning-rate" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "derivatives" ], "summary": "Original Screening question.", "path": "item/ml-screen-gradient-descent-foundation-updates/", "url": "https://mlprep.iwase.dev/item/ml-screen-gradient-descent-foundation-updates/" }, { "id": "ml-screen-gradient-descent-standard-convergence", "title": "Learning-rate stability", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "gradient-descent", "topic_title": "Gradient Descent", "skills": [ "gradient-update", "learning-rate", "convergence" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "quadratics", "inequalities" ], "summary": "Original Screening question.", "path": "item/ml-screen-gradient-descent-standard-convergence/", "url": "https://mlprep.iwase.dev/item/ml-screen-gradient-descent-standard-convergence/" }, { "id": "ml-screen-gradient-descent-advanced-sgd", "title": "SGD unbiasedness and variance", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "gradient-descent", "topic_title": "Gradient Descent", "skills": [ "gradient-update", "sgd" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 9, "prerequisites": [ "expectation", "variance" ], "summary": "Original Screening question.", "path": "item/ml-screen-gradient-descent-advanced-sgd/", "url": "https://mlprep.iwase.dev/item/ml-screen-gradient-descent-advanced-sgd/" }, { "id": "ml-screen-optimization-foundation-stationary", "title": "Stationary point and convexity", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "optimization", "topic_title": "Optimization", "skills": [ "objective-functions", "optimization-stationary-points", "optimization-convexity" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "derivatives" ], "summary": "Original Screening question.", "path": "item/ml-screen-optimization-foundation-stationary/", "url": "https://mlprep.iwase.dev/item/ml-screen-optimization-foundation-stationary/" }, { "id": "ml-screen-optimization-standard-l1-l2", "title": "Compare L1 and L2 solutions", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "optimization", "topic_title": "Optimization", "skills": [ "objective-functions", "optimization-stationary-points", "optimization-convexity", "optimization-regularization" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "subgradients", "derivatives" ], "summary": "Original Screening question.", "path": "item/ml-screen-optimization-standard-l1-l2/", "url": "https://mlprep.iwase.dev/item/ml-screen-optimization-standard-l1-l2/" }, { "id": "ml-screen-optimization-advanced-diagnostics", "title": "Diagnose optimization traces", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "optimization", "topic_title": "Optimization", "skills": [ "objective-functions", "optimization-regularization", "optimization-diagnostics" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "gradient-norm", "hessian", "regularized-objectives" ], "summary": "Original Screening question.", "path": "item/ml-screen-optimization-advanced-diagnostics/", "url": "https://mlprep.iwase.dev/item/ml-screen-optimization-advanced-diagnostics/" }, { "id": "ml-screen-neural-networks-foundation-forward", "title": "ReLU forward pass", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "neural-networks", "topic_title": "Neural Networks", "skills": [ "forward-pass", "activations" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "matrix-vector-multiplication", "relu" ], "summary": "Original Screening question.", "path": "item/ml-screen-neural-networks-foundation-forward/", "url": "https://mlprep.iwase.dev/item/ml-screen-neural-networks-foundation-forward/" }, { "id": "ml-screen-neural-networks-standard-backprop", "title": "Backpropagate through tanh", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "neural-networks", "topic_title": "Neural Networks", "skills": [ "forward-pass", "activations", "neural-network-chain-rule", "backpropagation", "neural-network-losses" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "chain-rule", "tanh-derivative" ], "summary": "Original Screening question.", "path": "item/ml-screen-neural-networks-standard-backprop/", "url": "https://mlprep.iwase.dev/item/ml-screen-neural-networks-standard-backprop/" }, { "id": "ml-screen-neural-networks-advanced-softmax", "title": "Parameters and output gradient", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "neural-networks", "topic_title": "Neural Networks", "skills": [ "neural-network-chain-rule", "backpropagation", "neural-network-losses", "dense-network-parameter-count" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 10, "prerequisites": [ "softmax-cross-entropy", "outer-products" ], "summary": "Original Screening question.", "path": "item/ml-screen-neural-networks-advanced-softmax/", "url": "https://mlprep.iwase.dev/item/ml-screen-neural-networks-advanced-softmax/" }, { "id": "ml-screen-convolution-foundation-operation", "title": "Valid convolution output", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "convolution", "topic_title": "Convolution", "skills": [ "convolution-operation" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "matrix-arithmetic" ], "summary": "Original Screening question.", "path": "item/ml-screen-convolution-foundation-operation/", "url": "https://mlprep.iwase.dev/item/ml-screen-convolution-foundation-operation/" }, { "id": "ml-screen-convolution-standard-shape", "title": "Convolution and pooling geometry", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "convolution", "topic_title": "Convolution", "skills": [ "output-shape", "padding", "stride", "convolution-parameter-count", "pooling" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "floor-function", "tensor-shapes" ], "summary": "Original Screening question.", "path": "item/ml-screen-convolution-standard-shape/", "url": "https://mlprep.iwase.dev/item/ml-screen-convolution-standard-shape/" }, { "id": "ml-screen-convolution-advanced-receptive-field", "title": "Receptive field through a stack", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "convolution", "topic_title": "Convolution", "skills": [ "output-shape", "pooling", "receptive-field" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 9, "prerequisites": [ "convolution-output-size", "stride" ], "summary": "Original Screening question.", "path": "item/ml-screen-convolution-advanced-receptive-field/", "url": "https://mlprep.iwase.dev/item/ml-screen-convolution-advanced-receptive-field/" }, { "id": "ml-screen-dimensionality-foundation-centering", "title": "Center data and find PCA direction", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "dimensionality-reduction", "topic_title": "Dimensionality Reduction", "skills": [ "centering", "covariance-eigendecomposition", "explained-variance" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 6, "prerequisites": [ "means", "covariance", "eigenvectors" ], "summary": "Original Screening question.", "path": "item/ml-screen-dimensionality-foundation-centering/", "url": "https://mlprep.iwase.dev/item/ml-screen-dimensionality-foundation-centering/" }, { "id": "ml-screen-dimensionality-standard-svd", "title": "Connect SVD to PCA", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "dimensionality-reduction", "topic_title": "Dimensionality Reduction", "skills": [ "covariance-eigendecomposition", "pca-via-svd", "explained-variance" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "svd", "eigenvalues" ], "summary": "Original Screening question.", "path": "item/ml-screen-dimensionality-standard-svd/", "url": "https://mlprep.iwase.dev/item/ml-screen-dimensionality-standard-svd/" }, { "id": "ml-screen-dimensionality-advanced-objective", "title": "Optimal rank-two PCA", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "dimensionality-reduction", "topic_title": "Dimensionality Reduction", "skills": [ "pca-objective", "covariance-eigendecomposition", "explained-variance" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "orthogonal-projection", "covariance-eigendecomposition" ], "summary": "Original Screening question.", "path": "item/ml-screen-dimensionality-advanced-objective/", "url": "https://mlprep.iwase.dev/item/ml-screen-dimensionality-advanced-objective/" }, { "id": "ml-screen-unsupervised-foundation-kmeans", "title": "One k-means iteration", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "unsupervised-learning", "topic_title": "Unsupervised Learning", "skills": [ "k-means", "clustering-objective" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "squared-distance", "means" ], "summary": "Original Screening question.", "path": "item/ml-screen-unsupervised-foundation-kmeans/", "url": "https://mlprep.iwase.dev/item/ml-screen-unsupervised-foundation-kmeans/" }, { "id": "ml-screen-unsupervised-standard-gmm", "title": "GMM responsibilities and M-step", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "unsupervised-learning", "topic_title": "Unsupervised Learning", "skills": [ "gaussian-mixtures", "expectation-maximization" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 9, "prerequisites": [ "bayes-rule", "weighted-means" ], "summary": "Original Screening question.", "path": "item/ml-screen-unsupervised-standard-gmm/", "url": "https://mlprep.iwase.dev/item/ml-screen-unsupervised-standard-gmm/" }, { "id": "ml-screen-unsupervised-advanced-local-optima", "title": "Compare k-means fixed points", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "unsupervised-learning", "topic_title": "Unsupervised Learning", "skills": [ "k-means", "clustering-objective" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 11, "prerequisites": [ "k-means", "squared-distance" ], "summary": "Original Screening question.", "path": "item/ml-screen-unsupervised-advanced-local-optima/", "url": "https://mlprep.iwase.dev/item/ml-screen-unsupervised-advanced-local-optima/" }, { "id": "ml-screen-theory-foundation-splits", "title": "Train, validation, and test roles", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "theory", "topic_title": "Theory", "skills": [ "generalization", "train-validation-test" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "supervised-learning-basics" ], "summary": "Original Screening question.", "path": "item/ml-screen-theory-foundation-splits/", "url": "https://mlprep.iwase.dev/item/ml-screen-theory-foundation-splits/" }, { "id": "ml-screen-theory-standard-bias-variance", "title": "Compute bias and variance", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "theory", "topic_title": "Theory", "skills": [ "bias-variance", "generalization" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "expectation", "variance", "squared-error" ], "summary": "Original Screening question.", "path": "item/ml-screen-theory-standard-bias-variance/", "url": "https://mlprep.iwase.dev/item/ml-screen-theory-standard-bias-variance/" }, { "id": "ml-screen-theory-advanced-complexity", "title": "Select model complexity", "collection": "screening", "area": "machine-learning", "area_title": "Machine Learning", "topic": "theory", "topic_title": "Theory", "skills": [ "bias-variance", "generalization", "overfitting", "train-validation-test", "model-complexity" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "error-rates", "data-splits" ], "summary": "Original Screening question.", "path": "item/ml-screen-theory-advanced-complexity/", "url": "https://mlprep.iwase.dev/item/ml-screen-theory-advanced-complexity/" }, { "id": "math-la-01-matrix-invariants", "title": "Matrix arithmetic and invariants", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "matrix-operations", "inverse", "determinant", "trace", "matrix-norms" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "matrix-arithmetic", "square-roots" ], "summary": "Original Screening question.", "path": "item/math-la-01-matrix-invariants/", "url": "https://mlprep.iwase.dev/item/math-la-01-matrix-invariants/" }, { "id": "math-la-02-system-subspaces", "title": "One reduction, four structural conclusions", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "linear-systems", "row-reduction", "rank", "subspaces", "rank-nullity" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "linear-combinations", "reduced-row-echelon-form" ], "summary": "Original Screening question.", "path": "item/math-la-02-system-subspaces/", "url": "https://mlprep.iwase.dev/item/math-la-02-system-subspaces/" }, { "id": "math-la-03-basis-coordinates", "title": "Detect dependence and use a basis", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "linear-independence", "basis-dimension", "change-of-basis" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 3, "prerequisites": [ "linear-combinations" ], "summary": "Original Screening question.", "path": "item/math-la-03-basis-coordinates/", "url": "https://mlprep.iwase.dev/item/math-la-03-basis-coordinates/" }, { "id": "math-la-04-orthogonal-projection", "title": "Projection and orthogonal residual", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "orthogonality", "projection" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 3, "prerequisites": [ "dot-products" ], "summary": "Original Screening question.", "path": "item/math-la-04-orthogonal-projection/", "url": "https://mlprep.iwase.dev/item/math-la-04-orthogonal-projection/" }, { "id": "math-la-05-qr-least-squares", "title": "Thin QR and a least-squares fit", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "qr", "least-squares" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "orthogonality", "triangular-systems" ], "summary": "Original Screening question.", "path": "item/math-la-05-qr-least-squares/", "url": "https://mlprep.iwase.dev/item/math-la-05-qr-least-squares/" }, { "id": "math-la-06-spectral-power", "title": "Diagonalize a symmetric matrix", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "eigenvalues", "eigenvectors", "diagonalization", "symmetric-matrices" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "determinants", "matrix-multiplication" ], "summary": "Original Screening question.", "path": "item/math-la-06-spectral-power/", "url": "https://mlprep.iwase.dev/item/math-la-06-spectral-power/" }, { "id": "math-la-07-quadratic-positive", "title": "Certify a quadratic form", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "quadratic-forms", "positive-definite-semidefinite" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "eigenvalues", "unit-vectors" ], "summary": "Original Screening question.", "path": "item/math-la-07-quadratic-positive/", "url": "https://mlprep.iwase.dev/item/math-la-07-quadratic-positive/" }, { "id": "math-la-08-svd-low-rank-pca", "title": "SVD, best rank one, and PCA energy", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "linear-algebra", "topic_title": "Linear Algebra", "skills": [ "svd", "low-rank-approximation", "pca-via-svd" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "orthogonality", "matrix-norms", "eigenvalues" ], "summary": "Original Screening question.", "path": "item/math-la-08-svd-low-rank-pca/", "url": "https://mlprep.iwase.dev/item/math-la-08-svd-low-rank-pca/" }, { "id": "math-calc-01-removable-limit", "title": "Limit, continuity, derivative, and integral", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "limits", "continuity", "single-variable-differentiation", "single-variable-integration" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 3, "prerequisites": [ "factoring-polynomials", "basic-integration" ], "summary": "Original Screening question.", "path": "item/math-calc-01-removable-limit/", "url": "https://mlprep.iwase.dev/item/math-calc-01-removable-limit/" }, { "id": "math-calc-02-gradient-direction", "title": "Gradient and a normalized direction", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "partial-derivatives", "gradient", "directional-derivative" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 3, "prerequisites": [ "derivatives", "dot-products" ], "summary": "Original Screening question.", "path": "item/math-calc-02-gradient-direction/", "url": "https://mlprep.iwase.dev/item/math-calc-02-gradient-direction/" }, { "id": "math-calc-03-jacobian-chain", "title": "Vector chain rule", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "jacobian", "multivariable-chain-rule" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "partial-derivatives", "matrix-multiplication" ], "summary": "Original Screening question.", "path": "item/math-calc-03-jacobian-chain/", "url": "https://mlprep.iwase.dev/item/math-calc-03-jacobian-chain/" }, { "id": "math-calc-04-second-order-taylor", "title": "A two-variable quadratic approximation", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "taylor-approximation", "hessian" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "partial-derivatives", "quadratic-forms" ], "summary": "Original Screening question.", "path": "item/math-calc-04-second-order-taylor/", "url": "https://mlprep.iwase.dev/item/math-calc-04-second-order-taylor/" }, { "id": "math-calc-05-critical-classification", "title": "Classify all stationary points", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "calculus-stationary-points", "unconstrained-optimization", "calculus-convexity" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 6, "prerequisites": [ "gradient", "hessian", "eigenvalue-sign-test" ], "summary": "Original Screening question.", "path": "item/math-calc-05-critical-classification/", "url": "https://mlprep.iwase.dev/item/math-calc-05-critical-classification/" }, { "id": "math-calc-06-lagrange-circle", "title": "Minimum under an equality constraint", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "constrained-optimization" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "partial-derivatives", "linear-equations" ], "summary": "Original Screening question.", "path": "item/math-calc-06-lagrange-circle/", "url": "https://mlprep.iwase.dev/item/math-calc-06-lagrange-circle/" }, { "id": "math-calc-07-implicit-slope", "title": "Implicit tangent slope", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "implicit-differentiation" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 3, "prerequisites": [ "chain-rule", "algebra" ], "summary": "Original Screening question.", "path": "item/math-calc-07-implicit-slope/", "url": "https://mlprep.iwase.dev/item/math-calc-07-implicit-slope/" }, { "id": "math-calc-08-least-squares-derivatives", "title": "Matrix derivative with a numeric check", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "calculus", "topic_title": "Calculus", "skills": [ "matrix-calculus" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "chain-rule", "matrix-multiplication", "gradient" ], "summary": "Original Screening question.", "path": "item/math-calc-08-least-squares-derivatives/", "url": "https://mlprep.iwase.dev/item/math-calc-08-least-squares-derivatives/" }, { "id": "math-prob-01-counting-events", "title": "Count a union of selection events", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "counting", "event-algebra", "inclusion-exclusion" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 3, "prerequisites": [ "combinations", "fractions" ], "summary": "Original Screening question.", "path": "item/math-prob-01-counting-events/", "url": "https://mlprep.iwase.dev/item/math-prob-01-counting-events/" }, { "id": "math-prob-02-bayes-base-rate", "title": "Bayes rule with a base rate", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "conditional-probability", "total-probability", "bayes-rule", "probabilistic-independence" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "event-probability", "fractions" ], "summary": "Original Screening question.", "path": "item/math-prob-02-bayes-base-rate/", "url": "https://mlprep.iwase.dev/item/math-prob-02-bayes-base-rate/" }, { "id": "math-prob-03-binomial-moments", "title": "A small discrete distribution", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "discrete-random-variables", "pmf", "named-distributions", "expectation", "variance" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 3, "prerequisites": [ "binomial-coefficients" ], "summary": "Original Screening question.", "path": "item/math-prob-03-binomial-moments/", "url": "https://mlprep.iwase.dev/item/math-prob-03-binomial-moments/" }, { "id": "math-prob-04-exponential-transform", "title": "Transform an exponential 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"original", "estimated_minutes": 6, "prerequisites": [ "conditional-probability", "expectation", "variance" ], "summary": "Original Screening question.", "path": "item/math-prob-05-joint-correlation/", "url": "https://mlprep.iwase.dev/item/math-prob-05-joint-correlation/" }, { "id": "math-prob-06-total-variance", "title": "Condition first, then remove conditioning", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "conditional-expectation", "law-total-expectation", "law-total-variance" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "conditional-probability", "expectation", "variance" ], "summary": "Original Screening question.", "path": "item/math-prob-06-total-variance/", "url": "https://mlprep.iwase.dev/item/math-prob-06-total-variance/" }, { "id": "math-prob-07-sum-clt", "title": "Approximate a binomial central range", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "sums-of-random-variables", "central-limit-theorem" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "bernoulli-distribution", "normal-standardization" ], "summary": "Original Screening question.", "path": "item/math-prob-07-sum-clt/", "url": "https://mlprep.iwase.dev/item/math-prob-07-sum-clt/" }, { "id": "math-prob-08-lln-interpretation", "title": "State the LLN operationally", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "law-of-large-numbers" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 2, "prerequisites": [ "sample-means", "convergence-in-probability" ], "summary": "Original Screening question.", "path": "item/math-prob-08-lln-interpretation/", "url": "https://mlprep.iwase.dev/item/math-prob-08-lln-interpretation/" }, { "id": "math-prob-09-gaussian-linear-map", "title": "Linear image of a multivariate Gaussian", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "probability", "topic_title": "Probability", "skills": [ "multivariate-gaussian", "transformations", "covariance" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "matrix-multiplication", "gaussian-distribution" ], "summary": "Original Screening question.", "path": "item/math-prob-09-gaussian-linear-map/", "url": "https://mlprep.iwase.dev/item/math-prob-09-gaussian-linear-map/" }, { "id": "math-stats-01-sample-mean", "title": "Sampling distribution and standard error", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "samples", 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"https://mlprep.iwase.dev/item/math-stats-02-shrunken-proportion/" }, { "id": "math-stats-03-exponential-mle", "title": "Build and maximize a likelihood", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "likelihood", "maximum-likelihood" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "exponential-distribution", "logarithms", "derivatives" ], "summary": "Original Screening question.", "path": "item/math-stats-03-exponential-mle/", "url": "https://mlprep.iwase.dev/item/math-stats-03-exponential-mle/" }, { "id": "math-stats-04-beta-posterior-map", "title": "Conjugate update and MAP", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "map", "bayesian-updating" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "bernoulli-likelihood", "beta-distribution" ], "summary": "Original Screening question.", "path": "item/math-stats-04-beta-posterior-map/", "url": "https://mlprep.iwase.dev/item/math-stats-04-beta-posterior-map/" }, { "id": "math-stats-05-uniform-mom", "title": "Method of moments for an endpoint", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "method-of-moments" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 2, "prerequisites": [ "sample-mean", "uniform-distribution" ], "summary": "Original Screening question.", "path": "item/math-stats-05-uniform-mom/", "url": "https://mlprep.iwase.dev/item/math-stats-05-uniform-mom/" }, { "id": "math-stats-06-z-confidence-interval", "title": "Confidence interval with known variance", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "confidence-intervals" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 3, "prerequisites": [ "standard-error", "normal-quantiles" ], "summary": "Original Screening question.", "path": "item/math-stats-06-z-confidence-interval/", "url": "https://mlprep.iwase.dev/item/math-stats-06-z-confidence-interval/" }, { "id": "math-stats-07-one-sided-z-test", "title": "Hypotheses, critical region, and p-value", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "null-alternative", "p-values", "critical-regions" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 4, "prerequisites": [ "sampling-distributions", "standard-error" ], "summary": "Original Screening question.", "path": "item/math-stats-07-one-sided-z-test/", "url": "https://mlprep.iwase.dev/item/math-stats-07-one-sided-z-test/" }, { "id": "math-stats-08-power-of-z-test", "title": "Type II error and power at an alternative", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "type-i-error", "type-ii-error", "statistical-power" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "null-alternative", "normal-standardization" ], "summary": "Original Screening question.", "path": "item/math-stats-08-power-of-z-test/", "url": "https://mlprep.iwase.dev/item/math-stats-08-power-of-z-test/" }, { "id": "math-stats-09-chi-square-fit", "title": "Chi-square goodness of fit", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "goodness-of-fit", "chi-square" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "hypothesis-tests", "expected-counts" ], "summary": "Original Screening question.", "path": "item/math-stats-09-chi-square-fit/", "url": "https://mlprep.iwase.dev/item/math-stats-09-chi-square-fit/" }, { "id": "math-stats-10-simple-regression", "title": "Fit a simple regression line", "collection": "screening", "area": "mathematics", "area_title": "Mathematics", "topic": "statistics", "topic_title": "Statistics", "skills": [ "simple-linear-regression" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "sample-means", "algebra" ], "summary": "Original Screening question.", "path": "item/math-stats-10-simple-regression/", "url": "https://mlprep.iwase.dev/item/math-stats-10-simple-regression/" }, { "id": "prog-cf-trace-sentinel", "title": "Trace a sentinel-based string scan", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "control-flow", "topic_title": "Control Flow", "skills": [ "conditionals", "loops", "strings" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 5, "prerequisites": [ "string iteration", "character predicates" ], "summary": "Original Screening question.", "path": "item/prog-cf-trace-sentinel/", "url": "https://mlprep.iwase.dev/item/prog-cf-trace-sentinel/" }, { "id": "prog-cf-summarize-runs", "title": "Summarize consecutive character runs", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "control-flow", "topic_title": "Control Flow", "skills": [ "conditionals", "loops", "strings" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 10, "prerequisites": [ "string indexing", "list append" ], "summary": "Original Screening question.", "path": "item/prog-cf-summarize-runs/", "url": "https://mlprep.iwase.dev/item/prog-cf-summarize-runs/" }, { "id": "prog-cf-split-top-level", "title": "Split only at top-level commas", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "control-flow", "topic_title": "Control Flow", "skills": [ "conditionals", "loops", "strings" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 14, "prerequisites": [ "string traversal", "loop invariants" ], "summary": "Original Screening question.", "path": "item/prog-cf-split-top-level/", "url": "https://mlprep.iwase.dev/item/prog-cf-split-top-level/" }, { "id": "prog-ds-container-plan", "title": "Choose a built-in container by operation", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "sequence-data-structures", "dictionaries", "sets", "stack", "queue", "heap", "hash-map" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 7, "prerequisites": [ "Python built-in containers" ], "summary": "Original Screening question.", "path": "item/prog-ds-container-plan/", "url": "https://mlprep.iwase.dev/item/prog-ds-container-plan/" }, { "id": "prog-ds-postfix-stack", "title": "Evaluate postfix arithmetic with a stack", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "stack" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 12, "prerequisites": [ "integer parsing", "valid postfix notation" ], "summary": "Original Screening question.", "path": "item/prog-ds-postfix-stack/", "url": "https://mlprep.iwase.dev/item/prog-ds-postfix-stack/" }, { "id": "prog-ds-first-binary-search", "title": "Find the first matching index by binary search", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "binary-search", "sequence-data-structures" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 12, "prerequisites": [ "sorted lists", "half-open intervals" ], "summary": "Original Screening question.", "path": "item/prog-ds-first-binary-search/", "url": "https://mlprep.iwase.dev/item/prog-ds-first-binary-search/" }, { "id": "prog-ds-bfs-distances", "title": "Compute unweighted distances with BFS", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "queue", "bfs", "dictionaries", "hash-map" ], "priority": "stretch", "difficulty": "medium", "origin": "original", "estimated_minutes": 18, "prerequisites": [ "adjacency lists", "graph reachability" ], "summary": "Original Screening question.", "path": "item/prog-ds-bfs-distances/", "url": "https://mlprep.iwase.dev/item/prog-ds-bfs-distances/" }, { "id": "prog-ds-iterative-dfs", "title": "Traverse reachable nodes with iterative DFS", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "dfs", "stack", "dictionaries", "sets" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 15, "prerequisites": [ "adjacency lists", "stack operations" ], "summary": "Original Screening question.", "path": "item/prog-ds-iterative-dfs/", "url": "https://mlprep.iwase.dev/item/prog-ds-iterative-dfs/" }, { "id": "prog-ds-merge-sorted-lists", "title": "Merge sorted lists with a heap", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "data-structures", "topic_title": "Data Structures", "skills": [ "heap", "sequence-data-structures" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 18, "prerequisites": [ "sorted lists", "heap push and pop" ], "summary": "Original Screening question.", "path": "item/prog-ds-merge-sorted-lists/", "url": "https://mlprep.iwase.dev/item/prog-ds-merge-sorted-lists/" }, { "id": "prog-py-alias-copy-equality", "title": "Trace aliasing, shallow copy, and equality", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "python", "topic_title": "Python", "skills": [ "python-sequence-semantics", "mutability", "aliasing", "shallow-copy", "value-equality" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "nested lists", "object identity" ], "summary": "Original Screening question.", "path": "item/prog-py-alias-copy-equality/", "url": "https://mlprep.iwase.dev/item/prog-py-alias-copy-equality/" }, { "id": "prog-py-mutable-default", "title": "Diagnose a mutable default argument", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "python", "topic_title": "Python", "skills": [ "python-syntax", "functions", "mutability", "aliasing" ], "priority": "foundation", "difficulty": "medium", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "function parameters", "list mutation" ], "summary": "Original Screening question.", "path": "item/prog-py-mutable-default/", "url": "https://mlprep.iwase.dev/item/prog-py-mutable-default/" }, { "id": "prog-py-list-complexity", "title": "Reason about list-operation complexity", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "python", "topic_title": "Python", "skills": [ "complexity", "python-syntax", "functions", "python-sequence-semantics" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 8, "prerequisites": [ "list membership", "list count", "asymptotic notation" ], "summary": "Original Screening question.", "path": "item/prog-py-list-complexity/", "url": "https://mlprep.iwase.dev/item/prog-py-list-complexity/" }, { "id": "prog-py-point-equality", "title": "Implement value equality for a mutable class", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "python", "topic_title": "Python", "skills": [ "classes", "value-equality", "mutability", "aliasing" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 15, "prerequisites": [ "methods", "object identity", "NotImplemented" ], "summary": "Original Screening question.", "path": "item/prog-py-point-equality/", "url": "https://mlprep.iwase.dev/item/prog-py-point-equality/" }, { "id": "prog-rec-digit-sum-trace", "title": "Trace recursive calls and returns", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "recursion", "topic_title": "Recursion", "skills": [ "base-case", "recursive-trace" ], "priority": "foundation", "difficulty": "easy", "origin": "original", "estimated_minutes": 6, "prerequisites": [ "integer division", "remainder", "call stack" ], "summary": "Original Screening question.", "path": "item/prog-rec-digit-sum-trace/", "url": "https://mlprep.iwase.dev/item/prog-rec-digit-sum-trace/" }, { "id": "prog-rec-palindrome-indices", "title": "Check a palindrome recursively by indices", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "recursion", "topic_title": "Recursion", "skills": [ "base-case", "recursive-implementation" ], "priority": "core", "difficulty": "medium", "origin": "original", "estimated_minutes": 10, "prerequisites": [ "string indexing", "recursive call stack" ], "summary": "Original Screening question.", "path": "item/prog-rec-palindrome-indices/", "url": "https://mlprep.iwase.dev/item/prog-rec-palindrome-indices/" }, { "id": "prog-rec-nested-total", "title": "Sum an arbitrarily nested integer list", "collection": "screening", "area": "programming", "area_title": "Programming", "topic": "recursion", "topic_title": "Recursion", "skills": [ "base-case", "recursive-implementation" ], "priority": "stretch", "difficulty": "hard", "origin": "original", "estimated_minutes": 15, "prerequisites": [ "type checks", "recursive decomposition" ], "summary": "Original Screening question.", "path": "item/prog-rec-nested-total/", "url": "https://mlprep.iwase.dev/item/prog-rec-nested-total/" } ] }