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CISC 371 Numerical Optimization for Artificial Intelligence Units: 3.00
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.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Requirements: 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]).
Exclusion CISC 351/3.0.
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Formulate given problems as optimization functions.
- Synthesize data and solution methods for optimization.
- Implement, test, and evaluate optimization methods.
- Interpret and explain methods and solutions of given problems.
- Evaluate and critique performance of algorithms.