Stat3612 Statistical Machine Learning

Course materials by Dr. Aijun Zhang


Lecture 1: Introduction (Slides; Python)

Lecture 2: Data Exploration (Slides; Python)

Lecture 3: Generalized Linear Models (Slides; Python)

Lecture 4: Feature Engineering (Slides; Python)

Lecture 5: Regularized Linear Models (Slides; Python)

Lecture 6: Generalized Additive Models (Slides; Python)

Lecture 7: Interpretable Machine Learning (Slides; Python)

Lecture 8: Tree-based Methods (Slides; Python)

Lecture 9: SVM, HyperOpt and AutoML (Slides; Python)

Lecture 10: Deep Neural Networks (Slides; Python)

Lecture 11: Explainable Neural Networks (Slides; Github)

Lecture 12: Unsupervised Learning (Slides)


Homework 1: PDF (Due: October 10, 2020)

Homework 2: PDF (Due: November 16, 2020)

Homework 3: PDF (Due: December 6, 2020)

Mid-term Test 1: Start: 4:00pm Oct 27 <---> 4:00pm Oct 28 (Openbook)

Mid-term Test 2: Start: 4:00pm Nov 24 <---> 4:00pm Nov 25 (Openbook)

Group Project: PDF (Due: October 30, 2020)

Group Oral: 1:30 -- 4:30pm (Tues) Dec 1, 2020