This course is intended to provide a broad practical introduction to the concepts and methods in the field of artificial intelligence. \u201dAI\u201d represents a very diverse field of problems and techniques – we will focus on topics and ideas with broad application. One thing that you should think about coming into this course is simply this – what is Artificial Intelligence?<\/p>\n
The main focus and takeaway from this course should be the systematic representation of knowledge and the manipulation of these representations for general problem solving. If a problem can be represented systematically, any number of algorithmic techniques can be leveraged against it. Many of these algorithms will seem natural if not utterly naive – but applying them to appropriately represented and realized problems lends them power and generalizability.<\/p>\n
The course will be divided roughly into three topics:<\/p>\n
Traditionally, this is done as a project based coursed (four group projects) and an independent final exam, usually done as a take-home. Typical project topics include:<\/p>\n
The textbook is not required but it’s a good resource to have generally – I recommend\u00a0Artificial Intelligence: A Modern Approach\u00a0<\/em>by Russell and Norvig.<\/p>\n These are constantly under development but I’ll try to keep the latest versions posted here. These parallel the versions available on the 520 page, but are generally aimed more at undergraduates.<\/p>\nNotes<\/strong><\/h5>\n
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