SC4/SM8 Advanced Topics in Statistical Machine Learning | |
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Term: | Hilary Term 2018, Jan 15 - Mar 9 |
Lectures: | Tue 2pm, Thu 4pm |
Part C Class Tutor: | Leonard Hasenclever |
Part C Teaching Assistant: | Sam Davenport |
Part C Classes: | Fri 1:30-3 and 3-4:30 in weeks 3,5,7,8, LG.04 |
Part C Problem Sheet Deadlines: | Mon 10 in weeks 3,5,7,8 |
MSc Classes: | Mon 11-12, weeks 3,5,7,8, LG.01 |
Part C Revision Classes: | TT week 2: Thu 3pm (SC4 2017 exam), LG.01 |
TT week 4: Mon 3pm (SC4 2016 Q1c,Q2bc; SM4 2017 Q1bc,Q2), LG.01 |
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 may be updated during the course and are thus best read on screen. Please email me any typos or corrections.
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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 this year's syllabus of SB2b Statistical Machine Learning, PCA and the basics of clustering, will be used (which is mainly taught in the first three lectures of SB2b, also in HT2018), but SB2b is not a prerequisite and background notes will be provided.
Unsupervised and Supervised learning basics.
Loss functions. Empirical risk minimization.
Convex optimization and support vector machines.
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.
Laplace Approximation. Variational Bayes. Topic Modelling.
Collaborative filtering models, probabilistic matrix factorization.
Gaussian processes for regression and classification. Bayesian optimization.
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.