Unsupervised Learning 2001 Course Web Page Gatsby Computational Neuroscience Unit University College London MSc Intelligent Systems

Code: COMPGI02

Year: MSc in Intelligent Systems, Gatsby Unit Core Course

Prerequisites: Some background in statistics, calculus, linear algebra, and computer science. Any student or researcher at UCL is welcome to attend the lectures. Students wishing to take it for credit should consult with the course lecturers (email: zoubin @ gatsby.ucl.ac.uk ).

Term: 1

Time: 10.00 to 13.00 Tuesdays

Location: Cruciform Building Room 2.01

Taught By:

Zoubin Ghahramani (50%)
Carl E Rasmussen (50%)

Aims: This course provides students with an in-depth introduction to unsupervised learning techniques. It presents probabilistic approaches to modelling and their relation to coding theory and Bayesian statistics. A variety of latent variable models will be covered including mixture models (used for clustering), dimensionality reduction methods, time series models such as hidden Markov models which are used in speech recognition and bioinformatics, independent components analysis, hierarchical models, and nonlinear models.  The course will  present the foundations of probabilistic graphical models (e.g. Bayesian networks and Markov networks) as an overarching framework for unsupervised modelling. We will cover Markov chain Monte Carlo sampling methods and variational approximations for inference. Time permitting, students will also learn about Gaussian processes and the fundamentals of Bayesian decision theory/reinforcement learning/optimal control.

Learning Outcomes:  To be able to understand the theory of unsupervised learning systems; to have in-depth knowledge of the main models used in UL; to understand the methods of exact and approximate inference in probabilistic models; to be able to recognise which models are appropriate for different real-world applications of machine learning methods.

Method: Lecture presentations with associated class problems.

Assessment:

• The course has the following assessment components:
  • Written Examination (2.5 hours, 50%)
  • Weekly Assignments (50%)
• To pass this course, students must:
  • Pass the Written Examination
  • Pass the Assignments
  • Obtain an overall pass mark for all sections combined