MA6630 - Introduction to Statistical Learning | ||||||||||
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| * The offering term is subject to change without prior notice | ||||||||||
Course Aims | ||||||||||
Statistical learning is a new interdisciplinary area, which has connections to a variety of subjects including statistics, applied mathematics and computer sciences. It has been successfully applied in pattern recognition, signal processing, data mining, bioinformatics and financial engineering, etc. This course presents an overview of many cutting-edge techniques and algorithms in statistical learning. The covered topics include linear and nonlinear classification and regression, support vector machine, kernel methods, model averaging, boosting, as well as high-dimensional data. This course will provide the students the fundamental ideas and intuition behind modern statistical learning methods. | ||||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||||
Continuous Assessment: 40% | ||||||||||
Examination: 60% | ||||||||||
For a student to pass the course, at least 30% of the maximum mark for the examination must be obtained. | ||||||||||
Examination Duration: 2 hours | ||||||||||
Detailed Course Information | ||||||||||
| MA6630.pdf | ||||||||||
