Module Information
Course Delivery
| Delivery Type | Delivery length / details |
|---|---|
| Seminar | 10 x 2 Hour Seminars |
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Report: Oral presentation and discussion of analytic report on scienctific paper(s) | 40% |
| Semester Assessment | Essay: topic in Intelligent Systems - 3000 words | 60% |
| Supplementary Assessment | Resubmission of failed/nonsubmitted coursework components or ones of equivalent value. | 100% |
Learning Outcomes
On successful completion of this module students should be able to:
Describe and use the basic principles of Artificial Intelligence and Machine Learning.
Be able to reflect on project needs.
Practically apply AI and ML principles to meet those needs.
Present the material they have learned in an informed, clear manner.
Demonstrate understanding and insight into the material that they are presenting.
Aims
This module introduces the key ideas in Artificial Intelligence and ensures that all students are at roughly the same level before moving on to the specialist modules.
Brief description
This module introduces the key ideas in Artificial Intelligence and ensures all students are at roughly the same level before moving on to the specialist modules.
Content
General introduction to Artificial Intelligence (AI), including discussion of what AI is, its history, definitions, and philosophical debates on the issue (the Turing test and the Chinese room). Ethical issues.
2. Search -8 hours
Why search is important in AI and how to go about it. This includes both informed and uninformed strategies. Evolutionary search.
3. Knowledge Representation - 2 hours
Ways of representing knowledge in a computer-understandable way. Semantic networks, rules. Examples of the importance of KR.
4. Propositional and First-Order Logic - 4 hours
The backbone of knowledge representation.
5. Rule-based Systems - 2 hours
How can human expertise be automated? How to build an expert system - system concepts and architectures. Rule-based systems: design, operation, reasoning, backward and forward chaining. Knowledge acquisition.
6. Neural networks and subsymbolic learning - 2 hours
We can find solutions using search, but how can we remember solutions, learn from them and adapt them to new situations? This will cover perceptrons, single-layer and multi-layer networks.
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | Inherent to subject |
| Communication | Seminar |
| Improving own Learning and Performance | Inherent to subject |
| Information Technology | Inherent to subject |
| Personal Development and Career planning | Encourages students to see roles in subject for career and personal development |
| Problem solving | Inherent to subject |
| Research skills | Essay |
| Subject Specific Skills | Advanced Artificial Intelligence skills |
| Team work |
Notes
This module is at CQFW Level 7