MA50263: Mathematics of machine learning
[Page last updated: 04 February 2022]
Academic Year: | 2021/2 |
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: |
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Assessment Summary: | CW 100% |
Assessment Detail: |
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Supplementary Assessment: |
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Requisites: |
Must have Programming ability in Python or other high-level language. Graduate level mathematics skills.
In taking this module you cannot take CM50265 |
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. |
Aims: | To teach Machine Learning, including theoretical background and tools for implementation, to statistical applied mathematicians. |
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
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Notes:
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