Cs 188 berkeley

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, cs 188 berkeley, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings.

Completed all homeworks, projects, midterms, and finals in 5 weeks. Created different heuristics. Helped pacman agent find shortest path to eat all dots. Created basic reflex agent based on a variety of parameters. Improved agent to use minimax algorithm with alpha-beta pruning. Implemented expectimax for random ghost agents. Improved evaluation function for pacman states.

Cs 188 berkeley

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Project 3. Last commit date.

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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.

Cs 188 berkeley

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.

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Packages 0 No packages published. Then, worked on changing noise and discount parameters to enact different policies. Project 2 due Mon, Feb 14, pm. Project 2. Project 0 due Mon, Jan 24, pm. Exam Prep 8 Recording Solutions. Section 12 Recording Solutions. Improved evaluation function for pacman states. Project 1. Section 4 Recording Solutions. Exam Prep 15 Recording Solutions.

This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems.

Utility Theory, Rationality, Decisions [pdf] [pptx]. Jan 27 4 - Local Search [pdf] [pptx] Ch. Project 2 due Mon, Feb 14, pm. Improved pacman features. Section 4 Recording Solutions. View all files. Releases No releases published. Section 2 Recording Solutions. Section 14 Recording Solutions. Exam Prep 1 Recording Solutions. Then, used reinforcement learning to approximate Q-Values.