CPSC 091R. Special Topics: Advanced topics in Machine Learning

This course will cover the foundations of modern deep learning and generative AI, through the lens of probabilistic modeling. Students will gain the necessary tools to implement modern machine-learning models, including: large-scale neural network design, latent variable models, Bayesian learning, autoregressive models, and model evaluation. We will consider applications in computer vision, natural language processing and physical sciences, among others. Previous exposure to basic topics in probability (probability mass and density functions, normal distribution, expectation, conditional probability, etc.) is strongly recommended.
This is a Group 3 course.
Natural science and engineering.
1.0 credit.
Spring 2026. Hope.
Catalog chapter: Computer Science
Department website: https://www.swarthmore.edu/computer-science

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