CS4S771 - Machine Learning and Autonomous Systems 01 Sep 2021 - 01 Oct 2028 | Version 2
Associated Module Information
| Module Code: | CS4S771 | ||
|---|---|---|---|
| Module Title: | Machine Learning and Autonomous Systems | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Computer Science | ||
| Module Leader: | Janusz Kulon | ||
| Module Team: | Carl Jones | ||
| First Intended Intake: | SEP 2019 | Final Year of Intake: | |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100992 - machine learning | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 2 |
|---|---|
| Valid From | 01 Sep 2021 |
| Valid To | 01 Oct 2028 |
Module Aims
To provide a broad introduction to machine learning and autonomous systems, approaches to their design and development, areas of application, available tools and the implications to society.
Content Summary
Introduction to Machine learning, Autonomous Systems, Agents and Reinforcement Learning.
Data preparation, data exploration and dimensionality reduction.
Pattern recognition: classification, clustering and prediction.
Neural Networks: regression, supervised learning, unsupervised learning, semi-supervised learning.
Agents, Multi-agent systems, Reinforcement learning.
Ethical considerations, privacy, interpretability and implications to society.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 24 |
| Practical classes and workshops | 24 |
| Independent Study | 80 |
| Directed Study | 72 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | To design and implement an autonomous system utilising appropriate methods, tools and models. |
| LO2 | To critically explain, compare and contrast machine learning techniques and their use in the support of the creation of autonomous systems. |
| LO3 | To explain societal and ethical issues associated with autonomous systems. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Presentation (Asynchronous) 1 | Apply practical knowledge of machine learning techniques to solve a constrained problem and report on findings in the style of an academic poster supported by an oral presentation. | 0 | 2000 | 50 | No | 40 |
| Asynchronous Assessment | Practical Written Work 1 | Apply practical knowledge of machine learning techniques to solve a constrained problem and report on findings in the style of an academic paper. | 0 | 2000 | 50 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | LO3 | |
| Presentation (Asynchronous) 1 | ✔ | ✔ | ✔ |
| Practical Written Work 1 | ✔ | ✔ | ✔ |