Berkeley AI MaterialsArtificial IntelligenceFree courseBeginner level

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, Bayesâ Nets and advanced applications. It will prepare students for deeper dives into programming AI through various techniques.

3.0

1 review

Created by: Daniel Klein

Produced in 2012

Quality Score

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Overall Score : 60 / 100

Course Description

Artificial Intelligence Awards Best Practical Course

Course materials developed for UC Berkeley's introductory artificial intelligence course, CS 188

Beginner Level

Time: 30h 30m

Pros

Cons

- 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.
- 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

Daniel Klein

3.0 Rating
1 Reviews

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).