B027506 - MACHINE LEARNING

You will be able to understand, design, and apply several machine learning techniques for supervised learning and (to a lesser extent) for unsupervised learning. You will be able to reproduce some of the innovative solution described in the recent literature and apply them to closely related problems. The course will serve as a foundation for further study in the many engineering and scientific areas where state-of-the-art solutions are based on (deep) learning algorithms.

Good knowledge of a programming language (preferably Python), and a solid background in mathematics (calculus, linear algebra, and probability theory) are necessary prerequisites to this course. Previous knowledge of fundamental ideas in supervised learning, probabilistic graphical models, optimization and statistics would be very useful but not strictly necessary.

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, the underlying theory, and the necessary background that you will usually find in the textbooks.

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.

Same as above except topics are limited to those covered in the first 2/3 of the course and you will not be asked to reimplement the methods or reproducing experimental results.