COSC425: Intro to Machine Learning

Course Time: M/W/F @ 2:15-3:05
CRN: 44874, Term: Fall 2020

Instructor: Dr. Alex Williams.
Office Hours: Tues/Thurs 2:00-4:00 (Reserve online!)
Teaching Assistant: Zhuohang Li (
Office Hours: By appointment (Book via e-mail.)
Teaching Assistant: Tuhin Das (
Office Hours: By appointment (Book via e-mail.)

OverviewScheduleReferencesOffice HoursTeam Project

Note: The materials on this webpage are available on Canvas.


Machine learning is concerned with computer programs that automatically improve their performance through experience. This course covers the theory and practice of machine learning from a variety of perspectives. We cover topics such as clustering, decision trees, neural network learning, statistical learning methods, Bayesian learning methods, dimension reduction, kernel methods, and reinforcement learning. Programming assignments include implementation and hands-on experiments with various learning algorithms.


Note: This schedule is subject to change.

1Aug 19Introduction |
Aug 21Defining ML | [HD] Sec 1.1 + 1.2
2Aug 24Decision Trees I | [HD] Chapter 1
Aug 26Decision Trees II |
Aug 28Limits and Eval I | [HD] Sec 2.1 - 2.4HW 1: Released
3Aug 31Limits and Eval II |
Sept 2IB Learning I | [HD] Ch.3
Sept 4IB Learning II | HW 1: Due
4Sept 7[ No Lecture ] Project 1: Released
Sept 9Perceptron I | [HD] Ch.4
Sept 11Perceptron II |
5Sept 14Linear Models I | [HD] Ch.7
Sept 16[ No Lecture ]
Sept 18Project 1: Q&A
6Sept 21Linear Models II | [HD] Ch.7 Project 1: Due
Sept 23Prob. Methods I | [TM] Ch.2 HW 2: Released
Sept 25Team Meeting TP: M1 Released
7Sept 28Prob. Methods II |
Sept 30Linear Classification I | [TM] Ch.3 HW 2: Due
Oct 2Building Datasets Project 2: Released
8Oct 5Linear Classification II |
Oct 7[ No Lecture ]
Oct 9Team Meeting TP: M1 Due
9Oct 12SVMs | [HD] Ch.7.7
Oct 14Kernel Methods | [HD] Ch.11
Oct 16Team Meeting on Canvas.
10Oct 19US Learning I | [HD] Ch.15 Project 2: Due
Oct 21US Learning II |
Oct 23Team Meeting HW 3: Released
11Oct 26Neural Nets I | [IG] Ch.6 & 9
Oct 28Neural Nets II | [IG] Ch.10 & 15
Oct 30Team Meeting TP: M2 Due
12Nov 2Ensemble Methods | [HD] Ch.13
Nov 4Active Learning | [BS] Ch.1-3
Nov 6Team Meeting HW3: Due
13Nov 9Interpretable ML | [CM] All HW4: Released
Nov 11Reinfor. Learning | [RS] Ch.1-6 Project 3: Due
Nov 13Team Meeting
14Nov 16[ Team Project Time ]
Nov 18[ Team Project Time ]
Nov 20Team Meeting TP: M3 Due
15Nov 23 The Future of ML
Nov 25 HW4: Due
Nov 27
Dec 4 Project Presentations TP: M4 Due


This course will make periodic reference to the following online references

Textbook References