Advanced Statistics for Machine Learning

This course covers advanced statistical principles foundational to modern machine learning, emphasizing both theoretical rigor and practical applications. Topics include exponential families, sufficiency, information theory, likelihood-based methods, and Bayesian inference. The course explores spectral methods, probabilistic graphical models, sufficient dimension reduction, contrastive learning, and embedding techniques, with applications in deep generative models and transformers.

Credits

3 credits

Course Code

MA 788