Syllabus

Topics

1. Foundations: learning from data, empirical risk minimization, generalization.
2. Prediction: linear and logistic regression, regularization, kernels.
3. Structure: clustering, dimensionality reduction, matrix factorization.
4. Representations: embeddings and generative models.

Assessment

Homework 40% · Labs 20% · Final project 40%. (Placeholder.)