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STATS 305B: Models and Algorithms for Discrete Data
Overview
Notes
Discrete Distributions and the Basics of Statistical Inference
Contingency Tables
Logistic Regression
Exponential Families
Generalized Linear Models
Bayesian Inference
Bayesian GLMs
Sparse GLMs
Mixture Models and EM
Hidden Markov Models
Variational Autoencoders
Demo: Neural Networks and VAEs
Recurrent Neural Networks
Transformers
Random Graphs Models
Denoising Diffusion Models
Assignments
HW0: PyTorch Primer
HW1: Logistic Regression
HW2: Bayesian GLMs
HW3: Hidden Markov Models
HW4: Large Language Models
References
References
Index