Reading the 27 lectures of CMU 10-301/601 through its nine public Spring 2026 homework bundles and the practical limits for independent learners.
Spring 2026 publishes material for 27 lectures and nine homework bundles; outsiders can do the core work but cannot access Panopto, Piazza, Gradescope, or official homework solutions.
HW1 is written and programming work: mathematical and CS foundations followed by a majority-vote classifier.
HW2 moves from hand-calculated entropy and mutual information to an end-to-end tree learner, predictor, and evaluator.
HW3 is written work: a decision-tree review followed by K-NN, Perceptron, and Linear Regression through inductive bias, errors, and model selection.
HW4 joins probabilistic interpretation, cross-entropy gradients, and implementation into one traceable training pipeline.
HW5 avoids automatic differentiation so learners must track forward shapes, caches, and backward gradients themselves.
HW6 combines generalization, MLE/MAP, probabilistic learning, fairness metrics, and social impact in one written assignment about assumptions and tradeoffs.
HW7 builds on HW5 backpropagation to address deep-model architecture and training failures, emphasizing diagnosis over merely adding layers.
HW8 connects states, actions, rewards, transitions, and value updates while separating environment dynamics, policy, and estimation error.
The final written assignment combines ensembles, clustering, representation, and recommendation to test whether you can choose a learning paradigm from problem structure.