Save To Worklist Outline/Syllabus

CPSC 340 103 (Lecture)

Machine Learning and Data Mining

Models of algorithms for dimensionality reduction, nonlinear regression, classification, clustering and unsupervised learning; applications to computer graphics, computer games, bio-informatics, information retrieval, e-commerce, databases, computer vision and artificial intelligence.

This course is eligible for Credit/D/Fail grading. To determine whether you can take this course for Credit/D/Fail grading, visit the Credit/D/Fail website. You must register in the course before you can select the Credit/D/Fail grading option.

Credits: 3

Location: Vancouver

Term 1 (Sep 04, 2018 to Nov 30, 2018)

Cr/D/F Grading Change Dates

Last day to change between Credit/D/Fail and percentage grading (grading options cannot be changed after this date): September 18, 2018


Withdrawal Dates
Last day to withdraw without a W standing : September 18, 2018
Last day to withdraw with a W standing
(course cannot be dropped after this date) :
October 12, 2018

TermDay Start TimeEnd TimeBuildingRoom
1 Mon Wed Fri12:0013:00Hugh Dempster Pavilion110
Instructor: GELBART, MICHAEL


Seat Summary
Total Seats Remaining:13
Currently Registered:107
General Seats Remaining:13
Restricted Seats Remaining*:0
-  Select one Tutorial from sections T1A, T1B, T1C, T1D, T1E, T1Z
-  GRAD students: If you wish to take introductory Machine Learning for graduate credit, you should register for 532M in term 1. If you wish to take introductory Machine Learning for undergrad credit, you should register for 340 in term 2. (The CPSC 340 seats in term 1 are reserved for undergrads to ensure that undergrads have an opportunity to take the course as well).

Book Summary :
Information for the books required for this section is not available.