"Bayesian Statistics & Machine Learning" Working Group at Northwestern Statistics

This page serves as a repository for resources of the 2021-2022 working group "Bayesian Statistics & Machine Learning" at Department of Statistics at Northwestern University. In Fall 2021, this is set to be held weekly/bi-weekly (flexible) on Wednesday 2pm-3:30pm.

We mainly follow the recently published textbook "Probabilistic Machine Learning: An Introduction" by Kevin Murphy at Google Research. The textbook pdf can be accessed here.

Several textbooks can also be used for reference, including (but not limited to):

1. Pattern Recognition and Machine Learning by Christopher M. Bishop
2. Information Theory, Inference, and Learning Algorithms by David J.C. MacKay
3. Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis
4. Learning Theory from First Principles by Francis Bach
5. An Introduction to Statistical Learning (Second Edition) by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
6. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second Edition) by Trevor Hastie, Robert Tibshirani, Jerome Friedman

We mainly focus on the statistical and computational perspectives of machine learning.

Schedule (tentative):

• 10/20 (Wed, 2pm-3:30pm, Tim):

  • Optimization (part of Chapter 8 of PML book); Google Colab example

• 10/27 (Wed, 2pm-3:30pm, Tim):

  • Optimization (continued); Google Colab example