Class Times: Mondays, 14:00--17:00 Location: Gordon Square (24), Room 105 Instructor: Massimiliano Pontil Email Contact : gi01@cs.ucl.ac.uk Course description
The course covers supervised approaches to machine learning. It starts by reviewing fundamentals of statistical learning and probabilistic pattern recognition followed by an in-depth introduction to various supervised learning algorithms such as Least Squares, Logistic Regression, Perceptron Algorithm, Backpropagation, Decision Trees, Instance-based Learning, 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, 60%) , 2) Coursework Section (5 pieces, 40%). To pass this course, students must obtain at least 40% on the coursework component and an average of at least 50% when the coursework and exam components of a course are weighted together.
Problem sets
Problem set #1: PDF (Due: Noon, October 21)
Problem set #2: PDF (Due: Noon, November 4)
Problem set #3: PDF (Due: Noon, November 23)
Problem set #4: PDF (Due: Noon, December 5)
Problem set #5: PDF (Due: Noon, December 16)
Syllabus
The schedule of the course is listed below. Follow the link for each class to find lecture slides.
Date Title Monday, October 3 Introduction to Supervised Learning Monday, October 10 Discriminative and Generative Models Monday, October 17 Optimization and Learning Algorithms Monday, October 24 Regularization, Kernels Monday, October 31 Elements of Learning Theory Monday, November 7 No lectures (reading week) Monday, November 14 Support Vector Machines / Bayesian Interpretations Monday, November 21 Trees-based Algorithms Monday, November 28 Boosting Monday, December 5 Neural Networks Monday, December 12 Multi-task Learning 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. Neural Networks for Pattern Recognition. Oxford Univ. Press, 1997.
- 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.