CS4S773 - Computational Applications of Artificial Intelligence 06 Apr 2022 - 01 Oct 2028 | Version 1
Associated Module Information
| Module Code: | CS4S773 | ||
|---|---|---|---|
| Module Title: | Computational Applications of Artificial Intelligence | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Computer Science | ||
| Module Leader: | Mabrouka Abuhmida | ||
| Module Team: | Andrew Ware | ||
| First Intended Intake: | SEP 2022 | Final Year of Intake: | 2027 |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100359 - artificial intelligence | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 06 Apr 2022 |
| Valid To | 01 Oct 2028 |
Module Aims
Aim is to build students confidence in numeric programming alongside providing an understanding of computational thinking.
To provide students with the practical knowledge of computational application used in artificial intelligence and managing complex datasets such that they can assess practical situations and interpret real-world applications.
Content Summary
- Perform mathematical operations in probability and statistics.
- Solve statistical problems in abstract form and critically interpret outcomes in a real-world context.
- Apply various computational artificial intelligence techniques to simple problems.
- Pblem solving, logical and probabilistic reasoning .
- Explore factors affecting complexity, performance, numeric, scalability and solution deliverability.
- Implement low-level data science functionality using a relevant programming language.
- Introduction to big data: big data types, importing structured and unstructured data, manipulating big data, producing reports, anomaly detection.
- Correlation and Regression
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 demonstrate knowledge and understanding of the essential facts, concepts, and principles of programming within a mathematical context. |
| LO2 | To utilise essential facts, concepts, principles and theories in the analysis, specification, design, planning, documentation, implementation, and evaluation of solutions. |
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) | Collect, analyse, and interpret a complex data set and present results. | 0 | N/A | 50 | No | 40 |
| Asynchronous Assessment | Practical Written Work 1 | Collect, analyse, and interpret a complex data set and present results. | 0 | 2000 | 50 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Practical Coursework 1 (Asynch) | ✔ | ✔ | |
| Practical Written Work 1 | ✔ | ✔ | |