Course materials by Dr. Aijun Zhang, Fall 2020
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