Machine Learning (934G5)
Machine Learning
Module 934G5
Module details for 2023/24.
15 credits
FHEQ Level 7 (Masters)
Module Outline
This module will equip students with knowledge and practical experience for building and evaluating machine learning models. The module will cover multiple learning categories including supervised learning, and a variety of algorithms will be covered (both traditional approaches and those that are state of the art, e.g. advanced neural networks). The module will involve exploring the mathematics behind each algorithm as well as hands-on work (with software libraries) on real data.
Module learning outcomes
Demonstrate comprehensive understanding of key aspects of machine learning and standard methods
Show awareness of relevant issues and current challenges in machine learning
Systematically and creatively build and evaluate machine learning models
Act autonomously in preparing data appropriately to address a given problem, selecting the most suitable techniques to address the problem, and communicating valid rationale for choices made
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | A2 Week 1 | 100.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Laboratory | 1 hour | 11111111111 |
| Spring Semester | Lecture | 2 hours | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
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