Course co-ordinator(s): Dr Ron Petrick (Edinburgh), Dr Chengjia Wang (Edinburgh), Talal Shaikh (Dubai), Radu-Casian Mihailescu (Dubai), John See (Malaysia).
Aims:
- To introduce the fundamental concepts and techniques of AI, including planning, search and knowledge representation
- To introduce the scope, subfields and applications of AI, topics to be taken from a list including natural language processing, expert systems, robots and autonomous agents, machine learning and neural networks, and vision.
- To develop skills in AI programming in an appropriate language
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: Elementary knowledge of logic at the level of undergraduate Computer Science. Knowledge of high-level programming language concepts..
Location: ALP, Dubai, Edinburgh, Malaysia.
Semester: 1.
Syllabus:
- Search algorithms (depth first search, breadth first search, uniform cost search, A* search)
- constraint satisfaction problems;
- games (min-max, alpha-beta pruning);
- logic, resolution, introductory logic programming
- knowledge representation – logic, rules, frames
- goal and data-driven reasoning
- practical rule-based programming
- Overview of main fields of AI (Vision, Learning, Knowledge Engineering)
- In depth view of one field of AI (e.g. Planning, Natural language)
- Autonomous agents
- Applications of AI
- AI programming
Learning Outcomes: Subject Mastery
Understanding, Knowledge and Cognitive Skills Scholarship, Enquiry and Research (Research-Informed Learning)
- Critical understanding of traditional AI problem solving and knowledge representation methods
- Use of knowledge representation techniques (such as predicate logic and frames).
- Critical understanding of different systematic and heuristic search techniques
- Practice in expressing problems in terms of state-space search
- Broad knowledge and understanding of the subfields and applications of AI, such as computer vision, machine learning and expert systems.
- Detailed knowledge of one subfield of AI (e.g. natural language processing, planning) and ability to apply its formalisms and representations to small problems
- Detailed understanding of different approaches to autonomous agent and robot architectures, and the ability to critically evaluate their advantages and disadvantages in different contexts.
- Practice in the implementation of simple AI systems using a suitable language.
Learning Outcomes: Personal Abilities
ndustrial, Commercial & Professional Practice Autonomy, Accountability & Working with Others Communication, Numeracy & ICT
- Identification, representation and solution of problems
- Time management and resource organisation
- Research skills and report writing
- Practice in the use of ICT, numeracy and presentation skills.
SCQF Level: 9.