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SDSC3004 - Computational Optimization

Offering Academic Unit
School of Data Science
Credit Units
3
Course Duration
One Semester
Pre-requisite(s)
Course Offering Term*:
Semester A 2022/23 (Tentative)

* The offering term is subject to change without prior notice
 
Course Aims

This course introduces students to algorithms and techniques for optimization and nonlinear programming problems. Students will learn important numerical optimization methods such as the gradient descent, the Newton’s method, the quasi-Newton’s methods for unconstrained optimization, and the methods for constrained optimization. The classic methods for machine learning such as the stochastic gradient descent and its acceleration techniques, will be covered as well.


Assessment (Indicative only, please check the detailed course information)

Continuous Assessment: 40%
Examination: 60%
Examination Duration: 2 hours
 
Detailed Course Information

SDSC3004.pdf

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School of Data Science