ACS-49533 credit hours

Introduction to Machine Learning

Applied Computer Science · 3 hrs Lecture

Offered: Fall Or Winter, Every Year

ACS-4953 prerequisites

11 courses appear in this chain, up to 4 levels deep. 1 is needed whichever route you take: ACS-3902

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

ACS-3902Database Systemsmin C

any one of

ACS-2814Applications of Database Systemsmin C

ACS-2914not in the 2026-27 calendarmin C

any one of

MATH-1101not in the 2026-27 calendarmin C

MATH-1103Introduction to Calculus Imin C

any one of

Pre-Calculus Mathematics 40S

MATH-0042Mathematics Access IImin C

any one of

A minimum grade of 65% in Pre-Calculus 30S

MATH-0041Mathematics Access Imin C+

permission of the Department Chair

MATH-0040not in the 2026-27 calendar

MATH-1104Introduction to Calculus IImin C

MATH-1103Introduction to Calculus Imin C

Its prerequisites are shown above.

MATH-1201Linear Algebra Imin C

any one of

Pre-Calculus Mathematics 40S

Applied Mathematics 40S

MATH-0042Mathematics Access II

Its prerequisites are shown above.

MATH-1401Discrete Mathematicsmin C

any one of

Pre-Calculus Mathematics 40S

a grade of 65% or higher in Applied Mathematics 40S

MATH-0042Mathematics Access II

Its prerequisites are shown above.

any 3 credit hour Statistics course at or 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 (DATA SCIENCE STREAM)

Counts toward 5 more as one option among several, not as a course you must take:

ACS 4-YEAR BA IN APPLIED COMPUTER SCIENCE
ACS 4-YEAR BSc IN APPLIED COMPUTER SCIENCE
ACS 4-YEAR BSc (SCIENTIFIC COMPUTING STREAM)
ACS BSc (HONOURS) IN APPLIED COMPUTER SCIENCE
PHYS HONOURS BSc (COMPUTATIONAL PHYSICS STREAM)

Read from the departments' own calendar pages, where 50 of 208 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)].

Lecture

From the 2026-27 undergraduate calendar. Always verify details on WebAdvisor or with an academic advisor before registering.