Fall 2022

UC Berkeley

UC Berkeley's CS 188 course covers the basic ideas and techniques for designing intelligent computer systems, emphasizing statistical and decision-theoretic modeling. By the course's end, students will have built autonomous agents that can make efficient decisions in a variety of settings.

This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.

By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.

See the syllabus for slides, deadlines, and the lecture schedule. Readings refer to fourth edition of AIMA unless otherwise specified.

**CS 61A or 61B**: Prior computer programming experience is expected (see below)**CS 70 or Math 55**: Familiarity with basic concepts of propositional logic and probability are expected (see below)

*CS61A AND CS61B AND CS70 is the recommended background.*

The required math background in the second half of the course will be significantly greater than the first half. The self-diagnostic assignment Homework 0 will help check your preparation.

Course programming assignments will be in Python. We do not assume that students have previous experience with the language, but we do expect you to learn the basics very rapidly. Project 0 is designed to teach you the basics of Python and how the CS 188 submission autograder works. Project 1 is a good representation of the programming level that will be required for subsequent projects in this class.

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Lecture slides and readings available at Calendar

Lecture recordings available on YouTube at CS 188 - Introduction to Artificial Intelligence

Projects available at Projects

Resources available at Resources

Bayes' Nets: IndependenceBayes' Nets: InferenceBayes' Nets: RepresentationBayes' Nets: SamplingConstraint Satisfaction Problem (CSP)Decision NetworksHidden Markov Model (HMM)Informed SearchLogistic RegressionMarkov Decision Process (MDP)Naive BayesNeural networkOptimizationParticle FilteringPerceptronProbabilityReinforcement learning (RL)Search with Other AgentsUninformed SearchValue of Information (VPI)