Machine Learning (934G5)
Machine Learning
Module 934G5
Module details for 2025/26.
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. It will cover multiple learning categories, including supervised learning, and various algorithms (both traditional and state-of-the-art approaches, 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 the principles and assumptions relevant to different machine learning methods
Identify possible issues with a given dataset, as well as implement and critique different data preparation and preprocessing techniques to mitigate those issues
Systematically and creatively build, train and evaluate machine learning models for a given problem, with autonomy and critical decision making
Demonstrate critical awareness of relevant issues and challenges in machine learning
| 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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