This is the website for PY 895, Machine Learning for Physicists This website will be updated with HWs and suggested readings. In the fall, the class will be based on our new review A highbias, lowvariance introduction to Machine Learning for physicsits. Please use the link for the updated version that I will email you. 
General course information: TTh 9:3011:00.

Useful Resources: 
Syllabus and Course Information:
pdf 
Weeks 1: Chapter 12/ Notebook 1. Other Viewing + Readings: Lectures 13 from Learning from Data. Chapters 13 of Information Theory, Inference, and Learning Algorithms. IPython Cookbook fourth feature recipe: Introduction to Machine Learning in Python with scikitlearn 
Weeks 2: Chapter 3 and 5. Homework 1: This is Homework 1. Due on Tuesday Sept 17. 
Weeks 34:Chapter 4 and 6. Homework 2: This is Homework 2. Due on Tuesday Oct 2. 
Weeks 57:Chapter 7 and 8. Homework 3: This is Homework 3. Due on Tuesday Oct 23. 
Weeks 79:Chapter 911. Homework 4: This is Homework 4. Due on Tuesday Nov 13. 
Weeks 911:Chapter 1214 Homework 5: Please turn in two page summary of your final project (1 per group). Final projects will be due Monday Dec. 17th. Summary due on Thursday Nov 29.Paper for class: Neal and Hinton EM paper. 
Weeks 1213:Chapter 1516. Homework 6: Notebook 16. Due on Thursday Dec 6th.Paper for class:Boltzmann Encoded Adversarial Machines . 