COMPM050  Advanced Topics in Machine Learning
This database contains the 201718 versions of syllabuses. Syllabuses from the 201617 session are available here.
Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).
Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).
Code  COMPM050 (Also taught as: COMPGI13) 

Year  4 (Masters) 
Prerequisites  Linear Algebra, Probability Theory, Calculus 
Term  1 
Taught By  Arthur Gretton (50%) and Carlo Ciliberto (50%) 
Aims  Kernel methodsTo gain an understanding of the theory and applications of kernel methods, including:
Learning theoryTo learn the fundamentals of statistical learning theory. In particular to:

Learning Outcomes  To gain indepth familiarity with the selected research topics, understand how to design and implement learning algorithms. To be able to individually read, understand and discuss research papers in the field. 
Content
Introduction to kernel methods:
 Definition of a kernel, how it relates to a feature space, The reproducing kernel Hilbert space
 Simple applications: kernel PCA, kernel ridge regression
 Distance between means in RKHS, integral probability metrics, the maximum mean discrepancy (MMD), twosample tests
 Choice of kernels for distinguishing distributions, characteristic kernels
 Covariance operator in RKHS: proof of existence, definition of norms (including HSIC, the HilbertSchmidt independence criterion)
 Application of HSIC to independence testing
 Feature selection, taxonomy discovery.
 Introduction to independent component analysis, kernel ICA
 Large margin classification, support vector machines for classification
Introduction to supervised learning in the context of statistical learning theory:
 a taxonomy of learning problems
 no free lunch theorem
 regularization
 model selection
 stability and generalization
 measures of complexity for hypotheses spaces
 sample complexity, generalization bounds
Method of Instruction
Frontal teaching using whiteboard and slides.
Assessment
The course has the following assessment components:
 Written Examination (50%)
 Coursework Section (50%)
To pass this module, students must:
 Obtain an overall pass mark of 50% for all components combined.
Resources
Reading list available via the UCL Library catalogue.