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CISC 271 Linear Methods for Artificial Intelligence Units: 3.00
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.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Requirements: 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]).
Exclusion MATH 272/3.0.
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Select and implement algorithms for vectorial data.
- Synthesize data and solution methods for principal-component analysis.
- Implement, test and evaluate methods for linear regression.
- Interpret and explain methods and solutions in data classification.
- Evaluate and critique performance of algorithms in data classification.