UC Berkeley CS188 Intro to AI
This UC Berkeley class is a semester look at the introductory concepts of artificial intelligence. It covers search, minimax, Markov decision processes, Bayes Nets and advanced applications. It will prepare students for deeper dives into programming AI through various techniques.
Created by: Daniel Klein
Produced in 2012
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Overall Score : 60 / 100
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Course Description
artificial intelligence Awards Best Practical Course
Course materials developed for UC Berkeley's introductory artificial intelligence course, CS 188
Pros
Cons
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- One of the best-established introductory courses in AI.
- Course is challenging, but the trial by fire creates a strong foundation for AI.
- Course teaches students how to train Pacman AI to show visible results for their improvements as they go.
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- Despite being an intro to AI, this course is not an intro to computer science. A coding background is pretty much mandatory.
- Course pace is relentless.
- Course relies heavily on students seeking their own supplemental resources.
Instructor Details
- 3.0 Rating
- 1 Reviews
Daniel Klein
Daniel Klein is a computer scientist and associate professor of computer science at the University of California, Berkeley. Professor Klein's research focuses on statistical natural language processing, including unsupervised learning methods, syntactic parsing, information extraction, and machine translation. He received his BA in Math, CS, Linguistics (summa cum laude) from Cornell University (1994-1998); an M.St. in Linguistics from Oxford University (1998-1999); and an M.S. and Ph.D. in Computer Science from Stanford University (1999-2004).
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