In this class you will learn about several fundamental and some advanced algorithms for statistical learning, you will know the basics of computational learning theory, and will be able to design state-of-the-art solutions to application problems. Broad topics that are covered include: Generalized linear models, kernel methods, learning in graphical models, ensemble techniques and boosting, unsupervised learning, deep learning.

A good knowledge of a programming language, and a solid background in mathematics (calculus, linear algebra, and probability theory) are necessary prerequisites to this course. Previous knowledge of optimization techniques and statistics would be useful but not strictly necessary.

[GBC16] I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016.

[HTF09] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Data Mining, Inference, and Prediction. 2nd edition. Springer, 2009.

[B12] D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.

[S18]O. Simeone (2018), A Brief Introduction to Machine Learning for Engineers, Foundations and Trends R in Signal Processing: Vol. 12, No. 3-4, pp 200–431. DOI: 10.1561/2000000102.

[RN10] Stuart Russell and Peter Norvig.Artificial Intelligence, A Modern approach (3rd revised Edition), Prentice Hall, 2010.

[STC00] John Shawe-Taylor and Nello Cristianini. Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000

There is a single oral final exam. You can choose the exam topic but you are strongly advised to discuss with me before you begin working on it. Typically, you will be assigned a set of papers to read and will be asked to reproduce some experimental results. You will be required to give a short (30 min) presentation during the exam. Please ensure that your presentation includes an introduction to the problem being addressed, a brief review of relevant literature, technical derivation of methods, and, if appropriate, a detailed description of experimental work. You are allowed to use multimedia tools to prepare your presentation. You are responsible for understanding all the relevant concepts and the underlying theory.

You can work in groups of two to carry out experimental works (three is an exceptional number that you must motivate clearly). If you do so, please ensure that personal contributions to the overall work are clearly identifiable.

There is a single oral final exam on a subset of topics (e.g. supervised learning, learning theory, grapical models).

Tuesday, 10:45-12:45

Please do not email me about office hours, just check the School of Engineering Website for (unlikely) changes

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