| Unit name | AI Tools and Methods |
|---|---|
| Unit code | COMS20020 |
| Credit points | 20 |
| Level of study | I/5 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Professor. Seth Bullock |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
COMS10016 Imperative and Functional Programming and COMS10018 Object-Oriented Programming and
|
| Units you must take alongside this one (co-requisite units) |
None. |
| Units you may not take alongside this one |
None. |
| School/department | School of Computer Science |
| Faculty | Faculty of Engineering |
Why is this unit important?
Artificial Intelligence (AI) is an applied discipline that deploys different kinds of intelligent system in the real world.This unit introduces students to the methods and tools that are employed within AI and addresses the range of challenges and approaches that are involved in building, testing, deploying, and evaluating AI systems. It provides a hands-on approach in labs that support the development of a novel AI system that exemplifies the issues covered by the unit.
How does this unit fit into your programme of study
This is a mandatory unit that is taken during TB2 in Year 2. This allows students to develop an understanding of the practice of artificial intelligence and how its tools and technologies are implemented and deployed.
An overview of content
This unit will teach you how to build artificial intelligence (AI) systems. It introduces key approaches in machine learning and intelligent agents and explores how to combine them, covering key AI tools and methods including
common libraries, pipelines and tooling, data management and visualization, and the use of high performance computing and cloud computing. The unit also considers challenges in deploying AI in the real world and engages with considerations of responsibility, safety, sustainability and regulation. The unit’s approach is hands-on, providing opportunity to develop AI systems in a principled and professional manner.
How will students, personally, be different as a result of the unit
Students will be aware of state of the art in applied AI and understand the benefits & limitations of different AI tools and methods, and how these can be responsibly deployed to address a variety of AI problems.
Learning Outcomes
On successful completion of this unit, students will be able to:
The unit includes lectures to introduce and explain the concepts that underpin the tools and methods covered in the unit. Alongside the lectures, a series of applied labs allow students to gain experience with the tools and methods.
Over the course of the unit, these labs support the incremental development of an AI system (e.g., a recommender system or intelligent assistant) that forms the basis for the unit’s assessment.
Tasks which help you learn and prepare you for summative tasks (formative):
Teaching will take place over Weeks 13-24.
The unit comprises a series of lectures introducing the tools and methods employed within AI alongside a series of practical labs engaging with these methods and tools. Labs include opportunities for students to discuss and check their progress on the learning outcomes of the unit and are designed to support students in developing an AI system that will be the foundation of the final summative assessment.
Tasks which count towards your unit mark (summative)
Students will be assessed via three elements:
- A mid-term video presentation or equivalent that will assess Learning Outcome 1 (worth 20% of the unit) - A series of laboratory-based assessments that will assess Learning Outcome 2 (worth 20% of the unit) - An end-of-term programming report that will assess Learning Outcomes 3, and 4 (worth 60% of the unit)
When assessment does not go to plan
Students will retake relevant assessments in a like-for-like fashion.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. COMS20020).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
