Search results: 127
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.
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.
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