SDSC3004 - Computational Optimization

Offering Academic Unit
School of Data Science
Credit Units
Course Duration
One Semester
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


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School of Data Science