MS4H20 - Advanced Tools and Techniques for Data Science 27 Jul 2022 - 31 Aug 2026 | Version 1
Associated Module Information
| Module Code: | MS4H20 | ||
|---|---|---|---|
| Module Title: | Advanced Tools and Techniques for Data Science | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematics | ||
| Faculty Sub Group: | Maths | ||
| Module Leader: | Ieuan Griffiths | ||
| Module Team: | Rebecca Peters, Samuel Jobbins, Angelica Pachon, Stephanie Perkins | ||
| First Intended Intake: | Final Year of Intake: | ||
| Date Closed: | |||
| Credit Value: | 10 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100359 - artificial intelligence | 100992 - machine learning | |
| HECOS Code Weighting: | 50 | 50 | |
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 27 Jul 2022 |
| Valid To | 31 Aug 2026 |
Module Aims
To provide an understanding of Deep Neural Networks, the underlying mathematics behind them, and the techniques for using them as a powerful machine learning tool.
To select appropriate architectures of Artificial Neural Networks, how optimisation algorithms work, and how to identify hyperparameters in order toto improve Networks performance.?Furthermore, the module looks to provide a foundational understanding of model deployment within a cloud computing environment.
Content Summary
This module focuses on the understanding and implementation of Artificial Neural Networks (ANN). It will start by reviewing classification algorithms studied during the Machine Learning module, as 'black box' models. It will then discuss limits of linear classifiers and how to tackle problems using multilayer feedforward networks. A thorough presentation of stochastic gradient descent and backpropagation algorithms will guide the students in the implementation of a simple neural network from scratch. The use of cutting-edge libraries to implement bigger architectures will lead to the creation of an image classifier. The module will conclude with the study and implementation of Convolutional Neural Networks (CNN).?Finally, students will explore cutting edge Big Data technologies and Cloud Computing Solutions, such as Microsoft Azure’s ML Platform/ Amazon Web Services. In doing so, students will understand the limitations of executing complex algorithms on local system hardware, and develop an understanding of model deployment in a cloud computing environment.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 8 |
| Seminar | 8 |
| Tutorial | 8 |
| Independent Study | 40 |
| Directed Study | 36 |
| Total Hours Selected | 100 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | To understand the concepts of Artificial Neural Network, loss function, optimisation algorithm, and how the backpropagation algorithm works. |
| LO2 | To implement from scratch simple Artificial Neural Networks and more sophisticated architectures using cutting-edge machine learning software libraries. |
| LO3 | To obtain hands on experience with model deployment in a cloud computing environment, utilising industry leading solution. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Practical Coursework 1 (Asynch) | Analyse a data source using suitable deep learning techniques and report appropriate conclusions from the results of the analysis and deployment. | 0 | 2000 | 100 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | LO3 | |
| Practical Coursework 1 (Asynch) | ✔ | ✔ | ✔ |