In this course, you will cover the representation, utilisation, and acquisition of knowledge. These are the cornerstones of AI. You will investigate how to take a real world problem and represent it in a computer, so that the computer can solve that problem. Utilising this knowledge, or acquiring new knowledge, is done by search. The basics of search and its use in planning will be covered. Machine learning will be covered, including the difference between induction and deduction and the similarities between machine learning and optimisation.

Course Outcomes

A student who successfully completes this course should be able to:

• Students can represent, in a declarative way, what it means for something to be a solution to a given problem.
• Students understand and can implement the main heuristic-search-based approaches to problem solving and their pro's and con's.
• Students can elicit knowledge and represent it in intermediate knowledge representations.
• Students understand data driven and goal driven inference and can program a declarative rule-based system.
• Students can represent knowledge in predicate calculus and prolog formats.
• Students understand machine learning bias and how that allows programs to learn.

The course information page can be found HERE.

Lecturers

• Yun Sing Koh, Room 303-485, ykoh@cs.auckland.ac.nz (Course Coordinator)
• Mike Barley, Room 303- 488, barley@cs.auckland.ac.nz
• Ian Watson, Room 303-493, ian@cs.auckland.ac.nz

Tutors

• Jordan Douglas, room 303-461, jdou557@aucklanduni.ac.nz

Timetable

Lectures

Wed 10:00AM - 11:00AM, 301-G050 (Science Chem, Room G050)
Thurs 10:00AM - 11:00AM, 301-G050 (Science Chem, Room G050)
Fri 10:00AM - 11:00AM, 301-G050 (Science Chem, Room G050)

Tutorials

Fri 2:00PM - 3:00PM, 303-G20 (Sci Maths & Physics, Room G20)

Class representatives

• Jasmine Clancy <jcla331@aucklanduni.ac.nz>
• Bill Bao <qbao775@aucklanduni.ac.nz>