Machine Learning An Applied Mathematics Introduction Pdf

MA50263: Mathematics of machine learning

Academic Year:	2019/0
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:	Semester 1
Assessment Summary:	CW 100%
Assessment Detail:	Computational Project (CW 50%) Research Project (CW 50%)
Supplementary Assessment:	Like-for-like reassessment (where allowed by programme regulations)
Requisites:	Must have Programming ability in Python or other high-level 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 (feed-forward, convolution, recurrent networks). Universal approximation theorem. Gradient descent * Graphical models (decision trees, random forests, Markov random fields, Boltzmann machines) * Bayesian non-parametric (Gaussian and Dirichlet process regression, hyper parameters) * Reinforcement learning * Shrinkage methods.
Programme availability:	MA50263 is Optional on the following programmes: Department of Mathematical Sciences RSMA-AFM16 : Integrated PhD Statistical Applied Mathematics TSMA-AFM17 : MRes Statistical Applied Mathematics TSMA-AFM08 : MSc Modern Applications of Mathematics TSMA-AFM16 : MSc Statistical Applied Mathematics

Machine Learning An Applied Mathematics Introduction Pdf

Source: https://www.bath.ac.uk/catalogues/2019-2020/ma/MA50263.html

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