COMPGI13 - Advanced Topics in Machine Learning
This database contains 2017-18 versions of the syllabuses. For current versions please see 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).
|Code||COMPGI13 (Also taught as: COMPM050);|
|Prerequisites||Linear Algebra, Probability Theory, Calculus|
Arthur Gretton (50%) and Carlo Ciliberto (50%)
To gain an understanding of the theory and applications of kernel methods, including:
To learn the fundamentals of statistical learning theory. In particular to:
|Learning Outcomes||To gain in-depth 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.|
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), two-sample tests
- Choice of kernels for distinguishing distributions, characteristic kernels
- Covariance operator in RKHS: proof of existence, definition of norms (including HSIC, the Hilbert-Schmidt 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
- model selection
- stability and generalization
- measures of complexity for hypotheses spaces
- sample complexity, generalization bounds
Method of Instruction
Frontal teaching using whiteboard and slides.
The course has the following assessment components:
- Written Examination (50%)
- Coursework Section (50%)
Reading list available via the UCL Library catalogue.