## Stat3612 Statistical Machine Learning

Course materials by Dr. Aijun Zhang, Fall 2020

Syllabus

**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