Description
Elements of linear algebra for artificial intelligence, including: vector spaces; matrix decompositions; principal components analysis; linear regression; hyperplane classification of vectorial data; validation and cross-validation.
Follow-On Courses
This course appears in the pre- or co-requisites for the following course(s):
Learning Hours
120 (36 Lecture, 84 Private Study)
Prerequisite
Level 2 or above and a minimum grade of C- (obtained in any term) or a 'Pass' (obtained in Winter 2020) in {[CISC 101/3.0 or CISC 110/3.0 or CISC 151/3.0 or CISC 121/3.0] and [MATH 110/6.0 or MATH 111/6.0* or MATH 112/3.0] and [MATH 120/6.0 or MATH 121/6.0 or (MATH 123/3.0 and MATH 124/3.0) or MATH 126/6.0]}.