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):
- Pattern Recognition and Machine Learning by Christopher M. Bishop
- Information Theory, Inference, and Learning Algorithms by David J.C. MacKay
- Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis
- Learning Theory from First Principles by Francis Bach
- An Introduction to Statistical Learning (Second Edition) by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- 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