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SDSC6001 - Statistical Machine Learning II

Offering Academic Unit
School of Data Science
Credit Units
3
Course Duration
One Semester
Pre-requisite(s)
Course Offering Term*:
Semester B 2019/20

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

This course focuses on the theoretical foundation and fundamental methods in unsupervised and supervised learning, including Support Vector Machines, Ensemble Methods, K-means, Spectral Clustering, Dimension Reduction, Regularization Methods, Neural Networks, and Deep learning methods as well as the discipline of applying Python to program and implement aforementioned algorithms and methods.

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

Continuous Assessment: 65%
Examination: 35%
Examination Duration: 2 hours
 
Detailed Course Information

SDSC6001.pdf

Useful Links

School of Data Science