SDSC6011 - Optimization for Data Science | ||||||||
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* 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 | ||||||||
SDSC6011.pdf | ||||||||
Useful Links | ||||||||
School of Data Science |