SDSC6001 - Statistical Machine Learning II | ||||||||||
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* 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 |