SDSC6011 - Optimization for Data Science

Offering Academic Unit
School of Data Science
Credit Units
Course Duration
One Semester
Course Offering Term*:
Semester A 2021/22

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

This course offers an introduction to optimization methods with applications in data science.  We will introduce the theoretical foundation and the fundamental algorithms for optimization and advanced optimization methods for large-scale problems arising in data science and machine learning applications. Course content includes linear and nonlinear programming, conic programming, convex analysis, Lagrangian duality theory, augmented Lagrangian methods, stochastic gradient descent. Students write their own implementation of the algorithms in a programming language and explore their performance on realistic data sets.

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

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


Useful Links

School of Data Science