Machine Learning (Fall 2025)

Schedule is subject to small adjustments.
Slides will be posted before each lecture (and might be updated slightly soon after the lecture).
Lecture/discussion recordings are available on Brightspace.

Date	Topics	Slides	Discussion Session
08/29	Overview; nearest neighbor classification; core ML concepts	slides, handouts (1-page, 4-page)	math exercises, solutions, slides (by Yue)
09/05	Linear regression; regression with nonlinear basis; regularized regression	slides	(by Dongze)
09/12	Linear classifiers; Perceptron; logistic regression; general optimization algorithms		(by Sayan)
09/19	Multiclass classification; kernel methods		(by Yi)
09/26	Neural networks; convolutional neural nets		(by Yue)
10/03	SVM; decision trees; boosting		(by Yi)
10/10	Fall Recess
10/17	Exam 1
10/24	Clustering; K-means; Gaussian mixture models; EM		(by Yi)
10/31	Dimensionality reduction and visualization; PCA; Markov models		(by Sayan)
11/07	Recurrent neural nets; attention; transformers; large language models		(by Dongze)
11/14	Multi-armed bandits; preference learning; learning in games		(by Sayan)
11/21	(Deep) reinforcement learning		(by Dongze)
11/28	Thanksgiving
12/05	Exam 2