SC4/SM8 Advanced Topics in Statistical Machine Learning
Term: Hilary Term 2018, Jan 15 - Mar 9
Lectures: Tue 2pm, Thu 4pm
Classes: TBD
Class Tutors: TBD
Part C Problem Sheet Deadlines: TBD
Part C Revision Classes: TBD

Course Materials

The course materials will consist of notes, slides, and Jupyter notebooks. Notes are not exhaustive and should be used in conjunction with the slides. All materials are frequently updated and are thus best read on screen. Please email me any typos or corrections.

Aims and objectives:

Machine learning is widely used across many scientific and engineering disciplines to construct methods for finding interesting patterns in large datasets, devising complex models and prediction tools. This course introduces several widely used machine learning techniques and describes their underpinning statistical principles and properties. The course studies both unsupervised and supervised learning and several advanced topics are covered in detail, including some state-of-the-art machine learning techniques. The course will also cover computational considerations of machine learning algorithms and how they can scale to large datasets.


A8 Probability and A9 Statistics.
Some material from SB2b Statistical Machine Learning will be used (which is mainly taught in the first two weeks of SB2b, also in HT2018)

Synopsis (working version):

Convex optimization and support vector machines. Loss functions. Empirical risk minimization.
Kernel methods and reproducing kernel Hilbert spaces. Representer theorem. Representation of probabilities in RKHS.
Nonlinear dimensionality reduction: kernel PCA, spectral clustering.
Probabilistic and Bayesian machine learning: mixture modelling, information theoretic fundamentals, EM algorithm, Probabilistic PCA.
Variational Bayes. Laplace Approximation.
Collaborative filtering models, probabilistic matrix factorization.
Gaussian processes for regression and classification. Bayesian optimization.

Textbooks and Background Reading

Background Review Aids:




Knowledge of Python is not required for this course, but some descriptive examples in lectures may be done in Python. Students interested in further Python training are referred to the free University IT online courses.