Statistical Machine Learning

MATH68292 Statistical Machine Learning 15 Semester 2 Postgraduate Taught Level 6 Rendani Mbuvha Unit coordinator Thomas House Unit coordinator School of Mathematics 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 An introduction to machine learning models and algorithms, both for unsupervised and supervised learning, with a focus on the underlying mathematical and statistical principles, while including some aspects of implementation and practice. Studying the relation of machine learning approaches with traditional statistical methods.  An introduction to machine learning models and algorithms, both for unsupervised and supervised learning, with a focus on the underlying mathematical and statistical principles, while including some aspects of implementation and practice. Studying the relation of machine learning approaches with traditional statistical methods.  The unit aims to:Provide an introduction to machine learning models and algorithms, both for unsupervised and supervised learning, with a focus on the underlying mathematical and statistical principles, while including aspects of implementation and practice.  A further aim is to study the relationship of machine learning approaches to traditional statistical methods.  On the successful completion of the course, students will be able to:<ol><li>Explain the principles underlying both probabilistic and algorithmic machine learning techniques</li><li>Select and identify appropriate methodology for data analysis at the interface of statistics and machine learning</li><li>Apply modern machine learning methods to supervised and unsupervised learning problems</li><li>Use R and/or Python to analyse data with the methods discussed in the course</li></ol> Part A - Nonlinear (non-neural network) machine learning approaches<ul><li>Supervised Machine Learning:</li></ul>      -  Classification, the Bayes classifier, and K-nearest neighbours      -   Gaussian processes      -   Decision trees and random forests<ul><li>Unsupervised Machine Learning:</li></ul>      -   Nonparametric clustering and   Topic models      -   Dimensionality reduction including manifold learningPart B - Nonlinear (neural network) machine learning approaches<ul><li>Single-layer neural networks and deep neural networks </li><li>Optimising neural networks (gradient descent, backpropagation)</li><li>Statistical properties (overparametrisation, regularisation, universal function approximation) </li><li>Standard models (e.g. auto-encoders) and diverse data types </li></ul> Teaching is composed of two hours of lectures per week and one tutorial class per week. Teaching materials will be made available online for reference and review. One week is reserved for coursework. 0 Other 30% 1 Written exam 70% Other = coursework data analysis project, with a 30% weighting Generic feedback will be provided after marks are released.  MATH38161 Multivariate Statistics and Machine Learning Pre-Requisite Recommended MATH48061 Multivariate Statistics Pre-Requisite Recommended MATH27720 Probability and Statistics 2 Pre-Requisite Recommended MATH27711 Linear Regression Models Pre-Requisite Recommended N C. M. Bishop and H. Bishop. 2024.  Deep learning: Foundations and concepts. Springer.https://link.springer.com/book/10.1007/978-3-031-45468-4   Trevor Hastie, Robert Tibshirani, Jerome Friedman The Elements of Statistical LearningData Mining, Inference, and Prediction  https://link-springer-com.manchester.idm.oclc.org/book/10.1007/978-0-387-84858-7 Carl Edward Rasmussen and Christopher K. I. Williams Gaussian Processes for Machine Learning https://ebookcentral-proquest-com.manchester.idm.oclc.org/lib/manchester/detail.action?docID=3338604 Wolfgang Karl Härdle, Léopold Simar, Applied Multivariate Statistical Analysis https://link-springer-com.manchester.idm.oclc.org/book/10.1007/978-3-030-26006-4 Ruye Wang, Introduction to Machine Learning From Math to Code https://www.cambridge.org/highereducation/books/introduction-to-machine-learning/D52B7B2F91C909DE461D86B498802155#overview  Lectures 22 Practical classes & workshops 12 0 116