Cs 188 berkeley

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

.

Applied machine learning to pacman games. Utility Theory, Rationality, Decisions [pdf] [pptx]. Section 7 Recording Solutions.

.

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 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.

Mimikyu fanart

Worked with Markov Decision Processes. Improved evaluation function for pacman states. Section 1 Recording Solutions. This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. Created different heuristics. Section 6 Recording Solutions. You switched accounts on another tab or window. Exam Prep 12 Recording Solutions. Notifications Fork 55 Star Then, used particle filtering to achieve the same result. Folders and files Name Name Last commit message. Apr 14 24 - Reinforcement Learning [pdf] [pptx] Ch. Releases No releases published. Skip to content.

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.

Worked with Markov Decision Processes. Implemented perceptron classifier and MIRA classifier to read handwritten digits. Branches Tags. Packages 0 No packages published. Section 11 Recording Solutions. Exam Prep 11 Recording Solutions. Report repository. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Last commit date. Improved agent to use minimax algorithm with alpha-beta pruning. Reload to refresh your session. Utility Theory, Rationality, Decisions [pdf] [pptx]. You signed in with another tab or window. Completed all homeworks, projects, midterms, and finals in 5 weeks.