CSCI 416/516: Introduction to Machine Learning

Fall 2022

Meeting place: ISC 1221
Meetings: 3:30PM-4:50PM (Monday and Wednesday)
Instructor: Qun Li (liqun@cs)

Office Hours: Tuesday 2PM-5PM (in person or zoom)

Course Description:

This introductory course on machine learning will give an overview of important concepts, techniques, and algorithms in machine learning, beginning with topics such as linear regression and ending up with more recent topics such as boosting, support vector machines, and expectation maximization. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work.


Familiarity with basic probability theory, linear algebra, and calculus.


No required textbook. Recommended but not required: Machine Learning: a Probabilistic Perspective by Kevin Patrick Murphy

Exam Date:
Dec. 13 (W) 9AM-12PM

Honor code:
Honor code applies to this class. Students are encouraged to work together to do homework problems. What is important is a student's eventual understanding of homework problems, and not how that is achieved. Students may consult any source, except for another student's final draft, in learning how to do homework problems. Students must state what sources they have consulted, with whom they have collaborated, and from whom they have received help.

Student Accessibility Services:
William & Mary accommodates students with disabilities in accordance with federal laws and university policy. Any student who feels they may need an accommodation based on the impact of a learning, psychiatric, physical, or chronic health diagnosis should contact Student Accessibility Services staff at 757-221-2512 or at to determine if accommodations are warranted and to obtain an official letter of accommodation. For more information, please see