Description
Computational methods for artificial intelligence, particularly using numerical optimization. Applications may include: unconstrained data optimization; linear equality constraints; constrained data regression; constrained data classification; evaluating the effectiveness of analysis methods.
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
Registration in a School of Computing Plan and a minimum grade of C- (obtained in any term) or a 'Pass' (obtained in Winter 2020) in (CISC 271/3.0 and [STAT 263/3.0 or STAT_Options]).