Slides and Notebooks for my Probabilistic Machine Learning Course
References and Acknowledgments
There are several excellent resources I heavily relied on to create this course. I would like to thank the authors of these resources for making them available to the public (in no particular order)
- Piyush Rai (IIT Kanpur) excellent course and slides on the same subject
- Philip Hennig (University of Tübingen) excellent course and slides on the same subject
- Kevin Murphy (Google) excellent book on the same subject
- Ben Lambert has a great book and Youtube videos on the same subject
- Aki Vehtari (Aalto University) excellent course and slides on the same subject
- Richard McElreath course on Statistical Rethinking
- Allen Downey (Olin College) excellent book on the same subject
- Sargur Srihari (University at Buffalo) excellent course and slides on the same subject
- Felix Machine Learning and Simulation YouTube channel
Course Outline
- Introduction and Logistics [slides][notebook], [AL notebook], [BO notebook]
- Distributions, Refresher [notebook]
- Maximum Likelihood Estimation for Univariate [slides][notebook]
- MLE Multivariate
- MAP estimation
- Bayesian Inference with conjugate priors
- MLE, MAP for Linear Regression
- Bayesian Linear Regression
- MLE, MAP for Logistic Regression
- Bayesian Logistic Regression (with Laplace Approximation for posterior)
- Bayesian Logistic Regression (with Probit apprximation for predictive)
- Sampling Methods (Monte Carlo, Rejection Sampling)
- Markov Chain Monte Carlo (Metropolis-Hastings)