| CODE | SOR5291 | ||||||||||||
| TITLE | Topics in Statistical Learning | ||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||
| MQF LEVEL | 7 | ||||||||||||
| ECTS CREDITS | 10 | ||||||||||||
| DEPARTMENT | Statistics and Operations Research | ||||||||||||
| DESCRIPTION | Statistical learning is the mathematical and probabilistic framework for techniques commonly used in machine learning. It has many modern applications in today's world, particularly in finance, artificial intelligence and other data-intensive applications. This unit seeks not only to explore such interesting applications, but also to go deep into the mathematical and statistical theory that underpins them. A student will be required to focus on a number of topics from the list which follows, depending on the topic chosen for the thesis: - Hidden Markov models, state space models and extensions - Decision trees and random forests - Bayesian networks - Clustering methods - KNN algorithm - Gaussian processes - Kernel smoothing methods - Support vector machines - Latent variable models - Artificial neural networks - Deep learning - Model Assessment (e.g. Bias-Variance decomposition, Vapnik-Chervonenkis theory, cross-validation) Study-unit Aims: The aim of the study-unit is to view methods in statistical learning from a mathematical and statistical perspective. The mathematical and statistical side are only one part of it however - the student will be encouraged to implement this methods via the preferred software such as R, or other relevant software. The methods covered will include a mix of unsupervised and supervised learning algorithms, the latter covering namely classification and regression problems. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Determine the challenges one faces when learning from real data which involves the formulation of suitable models and justify the choice of such models; - Analyse the statistical and mathematical theory underlying machine learning techniques which will help the student bridge the gap between the two areas; - Be fluent in the conceptual underpinning of statistical learning methods, and hence be able to identify and justify which method(s) might be more valuable than others in conducting data analysis on real life datasets. 2. Skills: By the end of the study-unit the student will be able to: - Use a software of choice to apply a number of statistical methods using different learning algorithms. - Discuss the strengths and drawbacks of different methods and hence decide which method is best suited for the data under study. - Review the literature to keep up to date with any newly developed techniques and understand how these techniques can overcome previously unsolved problems and hence apply them to data sets. Main Text/s and any supplementary readings: Main Texts: - Barber, D. (2010). Bayesian Reasoning and Machine Learning. Cambridge University Press. (Available online) - Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference and Prediction (Second Edition). Springer. - James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013) An Introduction to Statistical Learning with Applications in R. Springer. - Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective (Adaptive and Machine Learning). The MIT Press. - Shalev-Shwartz, S. and Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. (Available online) Supplementary Readings: - Elliot, R.J., Aggoun, L., Moore, J.B. (1995). Hidden Markov models: estimation and control. Springer. - Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep learning. The MIT Press. |
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| ADDITIONAL NOTES | Pre-requisite Study-units: SOR2230, SOR3221 (or equivalent) | ||||||||||||
| STUDY-UNIT TYPE | Independent Study | ||||||||||||
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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