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 and in game playing will be covered.
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.
- Mike Barley, Room 303- 488, firstname.lastname@example.org (Course Coordinator)
- Pat Riddle, Room 490, email@example.com
- Ian Watson, Bldg 810 Room 829, firstname.lastname@example.org
James Garner, Room 303-476, email@example.com
Alex Peng, Room 303-476, firstname.lastname@example.org
Tues 2:00PM - 3:00PM, 301-G050 (Science Chem, Room G050)
Wed 2:00PM - 3:00PM, 301-G050 (Science Chem, Room G050)
Fri 2:00PM - 3:00PM, 301-G050 (Science Chem, Room G050)
- Wed 11:00AM - 12:00PM, 301-G050 (Science Chem, Room G050)
The syllabus page shows a table-oriented view of course schedule and basics of course grading. You can add any other comments, notes or thoughts you have about the course structure, course policies or anything else.
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