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 cs 188 berkeley 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.

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.

Cs 188 berkeley

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Improved evaluation function for pacman states. Exam Prep 11 Recording Solutions. Dismiss alert.

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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. Readings refer to fourth edition of AIMA unless otherwise specified. These links will work only if you are signed into your UC Berkeley Google account. The recordings are also available on Kaltura , which is a service that UC Berkeley partners with that facilitates the cloud recordings of Zoom meetings.

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.

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Section 6 Recording Solutions. Applied machine learning to pacman games. Section 9 Solutions. Your machine learning algorithms will classify handwritten digits and photographs. Implemented perceptron classifier and MIRA classifier to read handwritten digits. Packages 0 No packages published. Jan 27 4 - Local Search [pdf] [pptx] Ch. Utility Theory, Rationality, Decisions [pdf] [pptx] Ch. Project 3. Project 2. Finally, implemented joint particle filtering algorithm with multiple reactive ghosts. Exam Prep 8 Recording Solutions. Section 8 Recording Solutions. Last commit date.

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

Started with value iteration agent. Exam Prep 11 Recording Solutions. Utility Theory, Rationality, Decisions [pdf] [pptx] Ch. Ch Artificial-Intelligence - Berkeley-CS Section 3 Recording Solutions. Then, used particle filtering to achieve the same result. You signed out in another tab or window. Section 4 Recording Solutions. Project 1 due Thu, Feb 3, pm. Jan 27 4 - Local Search [pdf] [pptx] Ch.