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Welcome to the Moodle page for MA222 Metric Spaces. All of the module content can be found over on the MA260 Norms, Metrics & Topologies moodle page.
- 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.
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.
MB ChB Phase 3 spans year 3 AND 4.
2017 Cohort are supported through year 4 via the MD30X-19/20 module Moodle space.
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
