CSCI 416/516 · Spring 2026 · Fundamentals of AI/ML

Overview

Machine learning (ML) is a set of techniques that allow computers to learn from data and experience, rather than requiring humans to specify the desired behaviour by hand. ML has become increasingly central both in AI as an academic field, and in industry. This course provides a broad introduction to some of the most commonly used ML algorithms. It also serves to introduce key algorithmic principles which will serve as a foundation for more advanced courses, such as Deep Learning.

Syllabus

The syllabus for both CSCI 416 and CSCI 516 is available.

Useful Links

The course website (this website).
The instructor’s homepage.

Important Dates

Agenda Item	Due Date	Time	Location
Project Proposal (See Instructions)	02/13	03/06	Blackboard
Midterm Exam (See Practice Midterm)	03/20	03/20	Integrated Science Center 3348
Project Report (See Instructions)	05/01	05/01	Blackboard
Final Exam	05/05	05/05	Integrated Science Center 3348

Announcements

The syllabus is available for both CSCI 416 and CSCI 516.

Where and When

Class will be held synchronously every week, including a combination of lectures and office hours. Students are encouraged to attend both the lectures and office hours each week. There will be two mandatory tests held. Students are also encouraged to reach out to the instructor(s) with any questions or concerns.

Instructional team

Instructor	Lecture Time	Lecture Location	Office Hours	Office Location	Email
Ashley Y. Gao	M/W/F: 11:00–11:50	Integrated Science Center 3348	W/F: 9:00–10:30	Integrated Science Center 2381	ygao18@wm.edu
TBD	N/A	N/A	TBD	TBD	TBD

Homeworks

This class will have 4 homeworks. Please come back to this page once it is announced during class that a homework is posted. Homeworks are collected using Blackboard.

Homeworks will be accepted up to 3 days late, but 10% will be deducted for each day late, rounded up to the nearest day. No credit will be given for assignments submitted after 3 days. Extensions will be granted only in special situations with valid proof (e.g., doctor’s note).

In each homework, there will be additional 1–2 question(s) required for graduate students but optional to undergraduate students. If an undergraduate student answers correctly to such questions, they will receive extra credits (in addition to the max 10 points).

#	Out	Due	Materials
1	02/20	03/06	[Homework #1] · LaTeX
2	03/06	03/20	[Homework #2] · LaTeX
3	03/20	04/03	[Homework #3] · LaTeX
4	04/23	04/17	[Homework #4] · LaTeX

Homework #1: 10 pts
Homework #2: 10 pts
Homework #3: 10 pts
Homework #4: 10 pts
Midterm: 20 pts
Final Exam: 20 pts
Final Project: 20 pts

Final letter grades will be given based on the following scale: A ≥ 93% > A- ≥ 90% > B+ ≥ 87% > B ≥ 83% > B- ≥ 80% > C+ ≥ 77% > C ≥ 73% > C- ≥ 70% > D+ ≥ 67% > D ≥ 65% > D- ≥ 60% > F. Grades may be curved at the instructor’s discretion.

Schedule

Suggested readings are optional; they are resources we recommend to help you understand the course material. All of the textbooks listed below are freely available online.

Note that this schedule is tentative and will be updated once a topic is covered in the lecture(s).

Bishop = Pattern Recognition and Machine Learning

ESL = The Elements of Statistical Learning

MacKay = Information Theory, Inference, and Learning Algorithms

Barber = Bayesian Reasoning and Machine Learning

MC = Multivariate Calculus

#	Dates	Topic	Materials	Instructor Notes
0	01/21, 01/23	Lecture: Introduction, K-Nearest Neighbors Tutorial: Curse of Dimensionality	Lecture: [Lecture #1], [Lecture #2] Tutorial: [Tutorial]	ESL: 1, 2.1–2.3, 2.5 Domingos, 2012. A few useful things to know about machine learning Breiman, 2001. Statistical Modeling: The Two Cultures

Practice Exams

20% of your total mark is allocated to a final project, which will require you to apply several algorithms to a challenging problem and to write a short report analyzing the results. You are allowed to collaborate with at most 2 classmates on the final project (max group size 3).