Requirements: Problem sets (30%), Midterm (30%), Final exam (40%).
There have been two major paradigms for artificial intelligence. Traditional AI has followed the knowledge-based approach, based around handcrafting large symbolic representations of knowledge of the relevant domain. Many of the recent successes of AI, however, have come from the statistical approach, in which simple patterns are automatically extracted from data corpora, and applied to the problem. We will study how both of these paradigms apply to the various AI tasks, the relative strengths and weaknesses of each approach, and possible methods for combining the approaches together.
Problem sets may be submitted either by email, or in hard-copy. Acceptable formats for email are ASCII text (much preferred), HTML, or Postscript. NO OTHER FORMATS WILL BE ACCEPTED.
Also, INCLUDE THE HOMEWORK IN THE BODY OF YOUR EMAIL MESSAGE, NOT AS AN ATTACHMENT.
Homeworks must be submitted at or before the beginning of class on the day due. Assignments will be accepted up to a week late, with a penalty of one point out of ten. No assignments will be accepted more than a week late.
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