Bayes AI Slides
Author
Vadim Sokolov
George Mason University
Spring 2025
Unit 1: Introduction: AI Today and in the Past. Probability and Bayes Rule
Unit 2: Utility and Decision Theory
Unit 3: Bayesian Inference with Conjugate Pairs: Single Parameter Models
Unit 4: Hierarchical Bayesian Models
Unit 5: Bayesian Hypothesis Tests
Unit 6: Stochastic Processes
Unit 7: Markov Chain Monte Carlo
Unit 8: Bayesian Regression: Linear and Bayesian Trees
Unit 9: Quantile Neural Networks for Reinforcement Learning and Uncertainty Quantification
10: Bayesian Double Descent and Model Selection: Modern Appriach to Bias-Variance Tradeoff
Unit 11: Bayesian Neural Networks and Deep Learning
No matching items
