MA50263: Mathematics of machine learning
[Page last updated: 15 October 2020]
Academic Year:  2020/1 
Owning Department/School:  Department of Mathematical Sciences 
Credits:  6 [equivalent to 12 CATS credits] 
Notional Study Hours:  120 
Level:  Masters UG & PG (FHEQ level 7) 
Period: 

Assessment Summary:  CW 100% 
Assessment Detail: 

Supplementary Assessment: 

Requisites: 
Must have Programming ability in Python or other highlevel language. Graduate level mathematics skills.
In taking this module you cannot ( take CM50264 OR take CM50265 ) 
Description:  Aims: To teach Machine Learning, including theoretical background and tools for implementation, to statistical applied mathematicians. Learning Outcomes: After taking this unit, students should be able to: * Demonstrate knowledge of modern machine learning techniques * Use computational tools for applying machine learning * Show awareness of the applications of these methods * Understand the mathematical models underlying machine learning algorithms and details of their implementation * Write the relevant mathematical arguments in a precise and lucid fashion. Skills: Problem Solving (T,F&A), Computing (T,F&A), independent study and report writing. Content: Introduction to machine learning (supervised vs unsupervised learning, generative vs discriminative models, validation, regression vs neural networks, computational tools in Python). Additional topics will be chosen from: * Neural networks (feedforward, convolution, recurrent networks). Universal approximation theorem. Gradient descent * Graphical models (decision trees, random forests, Markov random fields, Boltzmann machines) * Bayesian nonparametric (Gaussian and Dirichlet process regression, hyper parameters) * Reinforcement learning * Shrinkage methods. 
Programme availability: 
MA50263 is Optional on the following programmes:Department of Mathematical Sciences

Notes:
