人工智能

Artificial Intelligence

UC Berkeley’s upper division course CS188: Introd…

加州大学伯克利分校

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人工智能

UC Berkeley’s upper division course CS188: Introduction to Artificial Intelligence now available to everyone online.

“Nothing short of awesome. This is a top-notch class that teaches you a lot of important concepts in optimization and AI, while making you feel like you’re on a wonderful adventure of discovery and fun.” edX student review

About this Course

Artificial intelligence is already all around you, from web search to video games. AI methods plan your driving directions, filter your spam, and focus your cameras on faces. AI lets you guide your phone with your voice and read foreign newspapers in English. Beyond today’s applications, AI is at the core of many new technologies that will shape our future. From self-driving cars to household robots, advancements in AI help transform science fiction into real systems.

The course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. CS188.1x focuses on Behavior from Computation and will cover the following areas:

  • Statistical and decision–theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in stochastic and in adversarial settings.
  • Reasoning and Learning. With this additional machinery your agents will be able to draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs.
  • Applications for a wide variety of artificial intelligence problems. The techniques you learn in CS188x will serve as the foundation for further study in any application area you choose to pursue.

Join us today to learn more about how AI affects your life, and where it is headed in the future.

Syllabus

Introduction
Overview
Agents: Perception, Decisions, and Actuation

Search and Planning
Uninformed Search (Depth-First, Breadth-First, Uniform-Cost)
Informed Search (A*, Greedy Search)
Heuristics and Optimality

Constraint Satisfaction Problems
Backtracking Search
Constraint Propagation (Arc Consistency)
Exploiting Graph Structure

Game Trees and Tree-Structured Computation
Minimax, Expectimax, Combinations
Evaluation Functions and Approximations
Alpha-Beta Pruning

Decision Theory
Preferences, Rationality, and Utilities
Maximum Expected Utility

Markov Decision Processes
Policies, Rewards, and Values
Value Iteration
Policy Iteration

Reinforcement Learning
TD/Q Learning
Exploration
Approximation

Prerequisites

Object-Oriented Programming, Recursion, Python or ability to learn Python quickly, Data Structures, Arrays, Hashtables, Stacks, Queues, Priority Queues, Traversal, Backpointers, Probability, Random Variables, and Expectations (Discrete), Basic Asymptotic Complexity (Big-O), Basic Counting (Combinations and Permutations)

Course Staff

Dan Klein

Dan Klein (PhD Stanford, MSt Oxford, BA Cornell) is an associate professor of computer science at the University of California, Berkeley. His research focuses on natural language processing and using computational methods to automatically acquire models of human languages. Examples include large-scale systems for language understanding, information extraction, and machine translation, as well as computational linguistics projects, such as the reconstruction of ancient languages. One of his best-known results was to show that human grammars can be learned by statistical methods. He also led the development of the Overmind, a galaxy-dominating, tournament-winning agent for the game of Starcraft. Academic honors include a Marshall Fellowship, a Microsoft Faculty Fellowship, a Sloan Fellowship, an NSF CAREER award, the ACM Grace Murray Hopper award for his work on grammar induction, and best paper awards at the ACL, NAACL, and EMNLP conferences. Professor Klein is the recipient of multiple teaching honors, including the UC Berkeley Distinguished Teaching Award.

Pieter Abbeel

Pieter Abbeel (PhD Stanford, MS/BS KU Leuven) joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in 2008. He regularly teaches CS188: Introduction to Artificial Intelligence and CS287: Advanced Robotics. His research focuses on robot learning. Some results include machine learning algorithms which have enabled advanced helicopter aerobatics, including maneuvers such as tic-tocs, chaos and auto-rotation, which only exceptional human pilots can perform, and the first end-to-end completion of reliably picking up a crumpled laundry article and folding it. Academic honors include best paper awards at ICML and ICRA, the Sloan Fellowship, the Air Force Office of Scientific Research Young Investigator Program (AFOSR-YIP) award, the Okawa Foundation award, the MIT TR35, the IEEE Robotics and Automation Society (RAS) Early Career Award, and the Dick Volz award for best PhD thesis in robotics and automation.

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