Introduction to Machine Learning
Applied Computer Science · 3 hrs Lecture
Offered: Fall Or Winter, Every Year
ACS-4953 prerequisites
8 courses appear in this chain, up to 2 levels deep. Every route through it involves a choice, so nothing is forced.
Part of this course's requisite text couldn't be parsed into structure, so the chain below may be incomplete. The full wording is under “Requisite courses”.
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all of
any one of
any one of
the former ACS-2914 with a minimum grade of C
any one of
Pre-Calculus Mathematics 40S
any one of
A minimum grade of 65% in Pre-Calculus 30S
a minimum grade of C+ in MATH-0041
permission of the Department Chair
the former MATH-0040
Minimum grade of C in MATH-1103
any one of
any 3 credit hour Statistics course at
above the 1000 level with a minimum grade of C
Add your completed courses to see which of these you've already done and what you could take next. They stay on this device.
Where ACS-4953 counts
Required by 1 program:
- STAT 4-YEAR BA/BSc IN STATISTICS
Counts toward 2 more as one option among several, not as a course you must take:
Read from the departments' own calendar pages, where 71 of 141 programs are only partly machine-readable, so treat these as a minimum rather than the full list. Confirm with an academic advisor.
Description
This course is an introduction to the broad field of machine learning. Machine learning provides the technical basis for data mining. This course examines the foundations and implementations of several machine learning algorithms. Specific topics include: rule and tree-based classifiers, bayesian models, clustering techniques and numeric prediction. Popular machine learning tool sets will be used to gain practical hands-on experience in i) preparing the data, ii) applying the various learning techniques and iii) interpreting the credibility of the results.
Requisite courses
ACS-3902, one of MATH-1101, MATH-1103, MATH-1104, MATH-1201, MATH-1401, and any 3 credit hour Statistics course at or above the 1000 level with a minimum grade of C [prerequisite(s)].
From the 2026-27 undergraduate calendar. Always verify details on WebAdvisor or with an academic advisor before registering.