Applied Machine Learning (G6061)
Note to prospective students: this content is drawn from our database of current courses and modules. The detail does vary from year to year as our courses are constantly under review and continuously improving, but this information should give you a real flavour of what it is like to study at Sussex.
We’re currently reviewing teaching and assessment of our modules in light of the COVID-19 situation. We’ll publish the latest information as soon as possible.
Applied Machine Learning
Module G6061
Module details for 2024/25.
15 credits
FHEQ Level 5
Pre-Requisite
some programming experience
Module Outline
This module provides a broad introduction to how machine learning methods can be applied to practical problems in different domains including natural language processing and computer vision. We will discuss aspects such as:
• how different types of data can be effectively pre-processed.
• the mappings between problems and machine learning tasks and loss functions
• system design considerations for different problems.
• metrics for evaluating the efficacy of predictions.
As we work through a range of real-world applications, we will describe a variety of unsupervised and supervised machine learning models including classical machine learning tools and modern deep learning techniques. Appropriate software packages will be introduced to enable students to design and implement their own systems.
Module learning outcomes
Determine the applicability of different machine learning models to data found in real-world applications.
Propose designs for simple systems, including appropriate pre-processing, to solve practical problems using machine learning.
Implement and document a computer program that learns and applies machine learning models to realistic data.
Critically evaluate the efficacy of proposed systems and appropriately communicate this analysis.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | A2 Week 2 | 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 | Lecture | 1 hour | 22222222222 |
| Spring Semester | Laboratory | 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.
Please note that the University will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the University keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.
The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.