SDSC5001 - Statistical Machine Learning I | ||||||||
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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 statistical machine learning, covering the key concepts of the probability theory and statistical inference for machine learning, classical and cutting-edge methods and theories for regression and classification, and popular methods for unsupervised learning. | ||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||
Continuous Assessment: 50% | ||||||||
Examination: 50% | ||||||||
Examination Duration: 2 hours | ||||||||
Detailed Course Information | ||||||||
| SDSC5001.pdf | ||||||||
Useful Links | ||||||||
| Department of Data Science | ||||||||
