Cs 188 berkeley

This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A cs 188 berkeley 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, cs 188 berkeley.

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.

Cs 188 berkeley

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Built Q-Learning agent and an Epsilon Greedy agent.

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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.

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. We make lecture recordings available as links to Google Drive, which you can find posted together with other materials on the Syllabus page of this website shortly after the lecture. These links will work only if you are signed into your UC Berkeley Google account.

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Worked with Markov Decision Processes. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Created basic reflex agent based on a variety of parameters. Section 8 Recording Solutions. Dismiss alert. Project 1. Project 1 due Thu, Feb 3, pm. Project 3. Project 5 due Fri, April 22, pm. Packages 0 No packages published. Latest commit. Then, used particle filtering to achieve the same result. Updated belief distribuition of ghost agents based on sequential noise readings and distribution of future ghost agent states. Exam Prep 8 Recording Solutions.

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

Applied machine learning to pacman games. Section 5 Recording Solutions. Reload to refresh your session. Project 6 due Fri, April 29, pm. Created different heuristics. Started with value iteration agent. Project 2 due Mon, Feb 14, pm. Moved pacman based on closest likely ghost location. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Ch You switched accounts on another tab or window. Your machine learning algorithms will classify handwritten digits and photographs.