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
Syllabus
Learning outcomes
By the end of the course, students should be able to do the following:
demonstrate critical understanding of traditional AI problem solving and knowledge representation methods
use knowledge representation techniques in practice (such as predicate logic and frames)
demonstrate critical understanding of different systematic and heuristic search techniques
practice expressing problems in terms of state-space search
apply knowledge of one subfield of AI (e.g. natural language processing, planning) and its formalisms and representations to solve small problems
demonstrate understanding of different approaches to autonomous agent architectures, and the ability to critically evaluate their advantages and disadvantages in different contexts.
implement simple AI systems using a suitable language
apply relevant skills for identification, representation and solution of problems
