The schedule below represents my current best estimate concerning due dates (and everything else). I am providing this information to give you a general idea of the pace and timing of the class. This schedule will certainly change as the semester progresses.

Unless otherwise noted, all readings are from

• Hands-On Machine Learning with Scikit-Learn and PyTorch, Aurélien Géron, 2025.

Readings followed by (S) are supplemental. You may find them helpful.

WEEK	DATE	TOPIC	READING/VIDEOS	OUT	IN
1	08-27	Introduction to ML decision_trees_part1.pdf	CH 1 through "Types of Machine Learning Systems" (p. 3-27)	Python Setup
2	09-01	Decision Trees Decision Tree Activity (.pdf)	CH 5 (p. 179-193)
	09-03	Numpy Lab	Fast Numpy Video The Numpy Array Numpy Numerical Operations Linear Algebra Review 1 through 2.3 (S)	PA1
3	09-08	Model Selection Testing And Validation Activity	CH 1 "Main Challenges of Machine Learning" to end (p. 27-38) Cross Validation (3.1) Leave One Out
	09-10	Probability and Expectation Bias/Variance Trade-off Activity	ISLP 2.2.1-2.2.2
4	09-15	Evaluating Classifiers	CH 3 through "Error Analysis" (p. 107-130)
	09-17	Ensembles: bagging, random forests, boosting ensemble_quiz.pdf	CH 6 through "Gradient Boosting" (p. 195-214)
5	09-22	EXAM 1
	09-24	Naive Bayes prob_naive.pdf	Joint Probability Marginalization Conditional Probability Independence Bayes’ Theorem Naïve Bayes	PA2	PA1
6	09-29	linear regression and gradient descent linear_regression.pdf linear_regression.py X_mpg.npy y_mpg.npy	Calculus Refresher (S) 9-14 Neural Net Video 1/2 Neural Net Video 2/2 deeplearningbook.org CH 4 through "Polynomial Regression" (p. 135-154)
	10-01	Logistic regression, likelihood, cross-entropy logistic_regression.zip	CH 4 "Logistic Regression" to end (p. 167-177)
7	10-06	Multi-layer perceptrons and backpropagation	CH 9 through "Building and Training MLPs with Scikit-Learn (p. 285-308)" 3Blue1Brown Videos 1-3 (S)
	10-08	FALL BREAK
8	10-13	PyTorch fundamentals			PA2
	10-15	EXAM 2
9	10-20	Network training: ReLU, initialization, regularization		Poster Project
	10-22	Computation graphs and automatic differentiation		PA3
10	10-27	Convolutional networks
	10-29	CIFAR-10 lab; poster project orientation			Poster Proposal
11	11-03	Recurrent networks: recurrence to attention
	11-05	Transformers			PA3 (11/8)
12	11-10	Transformer activity
	11-12	EXAM 3
13	11-17	Principal component analysis
	11-19	Nonlinear dimensionality reduction			Poster Bibliography
14	11-24	THANKSGIVING
	11-26	THANKSGIVING
15	12-01	Clustering
	12-03	Poster studio			Poster Checkpoint
16	12-08	EXAM 4
	12-10	Poster peer review and revision			Poster Draft
17	12-17	Poster Session and Presentations