杏吧论坛

Dr Konstantinos Kalogeropoulos

This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Health Data Science, MSc in Quantitative Methods for Risk Management, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (杏吧论坛 and Fudan), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available as an outside option to students on other programmes where regulations permit.

This course has a limited number of places (it is controlled access) and demand is typically very high. Priority is given to Department of Statistics students and those with the course listed in their programme regulations.

Basic knowledge in probability in statistics via a course such as the ST202 Probability Distribution Theory and Inference or an equivalent course; Previous programming experience is not required but students who have no previous experience in Python must complete an online pre-sessional Python course from the Digital Skills Lab before the start of the course (https://moodle.lse.ac.uk/course/view.php?id=7696)

The course sets up the foundations and covers the basic algorithms covered in probabilistic machine learning. Several techniques that are probabilistic in nature are introduced and standard topics are revisited from a Bayesian viewpoint. The module provides training in state-of-the-art methods that have been applied successfully for several tasks such as natural language processing, image recognition and fraud detection.

The first part of the module covers the basic concepts of Bayesian Inference such as prior and posterior distribution, Bayesian estimation,  model choice and forecasting. These concepts are also illustrated in real world applications modelled via linear models of regression and classification and compared with alternative approaches.

The second part of the module introduces and provides training in further topics of probabilistic machine learning such as Graphical models, mixtures and cluster analysis, Variational approximation, advanced Monte Carlo sampling methods, sequential data and Gaussian processes. All topics are illustrated via real-world examples and are contrasted against non-Bayesian approaches.

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