GI01/M055: Supervised Learning, Fall 2014

Course description

The course covers supervised approaches to machine learning. It starts by probabilistic pattern recognition followed by an in-depth introduction to various supervised learning algorithms such as Least Squares, Lasso, Perceptron Algorithm, Support Vector Machines and Boosting.

Prerequisites

Calculus, basic probability, basic linear algebra.

Grading

The course has the following assessment components: 1) Written Examination (2.5 hours, 75%) , 2) Coursework Section (2 pieces, 25%). To pass this course, students must obtain an average of at least 50% when the coursework and exam components of a course are weighted together.

Problem sets (Two - to be announced)

Coursework #1 (lab 1): (Due 16 January 2015)


Syllabus

The schedule of the course is listed below. Follow the link for each class to find lecture slides.




Date	Title
Monday, September 29	Introduction to Supervised Learning
Monday, October 6	Statistical Learning Theory (DRAFT)
Monday, October 13	Kernels and Regularisation (DRAFT)
Monday, October 20	Lab: Applying Regresion
Monday, October 27	Support vector machines (DRAFT)
Monday, November 3	No lectures (reading week)
Monday, November 10	Graph-based Semi-Supervised Learning
Monday, November 17	Proximal Methods (DRAFT)
Monday, November 24	Multi-task Learning (DRAFT)
Monday, December 1	Sparsity-based Methods
Monday, December 8	Online Learning (DRAFT)

Reading list

Main reference:
• T. Hastie, R. Tibshirani and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2002.

Other suggested references:

• C.M. Bishop. Pattern Recognition and Machine Learning Springer, 2006.
• N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines Cambridge University Press, 2001.
• R.O. Duda, P.E. Hart and D.G. Stork. Pattern Classification. Wiley, 2nd edition, 2004.
• D.J.C. MacKay. Information Theory, Pattern Recognition and Neural Networks. Cambridge Press, 2003
• T. Mitchell. Machine Learning. McGraw Hill, 1997
• J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
• B.Scholkopf and A.J. Smola. Learning with Kernels. MIT Press, 2002.
• V.N. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.

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