CSCI 416: Introduction to Machine Learning
Fall 2020
Meeting place: Zoom --- RSOF: Predominantly remote and
predominantly synchronous, off-campus
Meetings: 8:00-9:20 (Tu. and Th.) on Zoom
Instructor: Qun Li (liqun@cs)
Office Hours:
1:00-2:30PM (Tu. and Th.) on 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.
Prerequisites:
Familiarity with basic probability theory, linear algebra, and
calculus.
Textbook:
No required textbook.
Recommended but not required:
Machine Learning: a Probabilistic Perspective
by Kevin Patrick Murphy
Exam Date:
Nov. 19 (Thursday) 2PM-5PM
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
sas@wm.edu to determine if accommodations are warranted and to
obtain an official letter of accommodation. For more information,
please see www.wm.edu/sas.