Class Times: Mondays, 14:00--17:00 Location: Malet Place Engineering Building, Room 1.20 Instructor: John Shawe-Taylor Office: 8.14, CS Building, Malet Place Assistant Instructor: Janaina Mourao-Miranda Office: 8.11, CS Building, Malet Place Email Contact : mailto:email@example.com
Course description (Back to top)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, Logistic Regression, Perceptron Algorithm, Support Vector Machines and Boosting.
Prerequisites (Back to top)Calculus, basic probability, basic linear algebra.
Grading (Back to top)The course has the following assessment components: 1) Written Examination (2.5 hours, 75%) , 2) Coursework Section (3 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 (Back to top)Problem set #1: PDF (Due: Noon, October 23; returned: November 2)
Problem set #2: PDF (Due: Noon, November 16; returned: December 7)
Problem set #3: PDF (Due: Noon, December 18; returned January 15)
Syllabus (Back to top)The schedule of the course is listed below. Follow the link for each class to find lecture slides.
Date Title Monday, October 5 Introduction to Supervised Learning Monday, October 12 Discriminative and Generative Models Monday, October 19 Optimization and Learning Algorithms Monday, October 26 Regularization/ Kernels Monday, November 2 Lab session (Room 1.05) Monday, November 9 No lectures (reading week) Monday, November 16 Learning Theory Monday, November 23 Support Vector Machines / Bayesian Interpretations Monday, November 30 Lab session (Room 1.05) Monday, December 7 Introduction to Neuroimaging and Application of Supervised Learning to Neuroimaging Monday, December 14 Tree-based Learning Algorithms and Boosting
Reading list (Back to top)
- 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.