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2020/21

Content

In this course we will study the mathematical foundations of Machine Learning, with an emphasis on the interplay between approximation theory, statistics, and numerical optimization. We will begin with a study of Statistical Learning Theory, including the concepts of Empirical Risk Minimization and VC dimension. We will then move on to the study of numerical Optimization methods, which provide the foundation of machine learning algorithms. We will then discuss the foundations of many modern developmets in deep learning, including sequence models, variational autoencoders, generative adversarial networks, and reinforcement learning. While the course will be theoretical in nature, you are encouraged to experiment with Python and machine learning packages. Regular lecture notes will be published on Moodle and the dedicated course website.

Intended Learning Outcomes

Upon completion of this module you should be able to:

Describe the problem of supervised learning from the point of view of function approximation, optimization, and statistics.
Identify the most suitable optimization and modelling approach for a given machine learning problem.
Analyse the performance of various optimization algorthms from the point of view of computational complexity (both space and time) and statistical accuracy.
Implement a simple neural network architecture and apply it to a pattern recognition task.
Summarize current developments in deep learning, including sequence models, generative models, robustness, and reinforcement learning.

2020/21

MA940:Coherent sheaves and cohomology

Google "Warwcik MA940 Coherent" to avoid Moodle.

I hope to give most of the lectures live in MS.01 acc. to the stated timetable. If I can, I will use Lecture capture and post some short video clips for those of you working from home. I set up this moodle page as storage for these posts.

Warwick Medical School

MB ChB active learning sandbox

Space to explore affordances of interactive tools in Moodle, share projects in progress.

Final versions can be exported to specific Moodle module spaces for use with students.

2020/21

MD40X:MBChB Phase 3 (Year 4)

MB ChB Phase 3 spans year 3 AND 4.

2017 Cohort are supported through year 4 via the MD30X-19/20 module Moodle space.

Uncategorised

MD907:Becoming an Effective Teacher

Welcome to the Becoming an Effective Teacher moodle space. The resources you need for your module are stored here and you are encouraged to access the pre-module activities from here to complete before you attend the module.

We look forward to welcoming you to the module on 23rd October 2017

Uncategorised

MM101: Moodle Manual

A useful space for taking screenshots for the online Moodle Manual.