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CS188.1x: Artificial Intelligence BerkeleyX ABOUT THIS COURSE CS188.1x is a new online adaptation of the first half of UC Berkeley's CS188: Introduction to Artificial Intelligence. The on- campus version of this upper division computer science course draws about 600 Berkeley students each year. 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. CS188.1x focuses on Behavior from Computation. It 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 stochastic and in adversarial settings. CS188.2x (to follow CS188.1x, precise date to be determined) will cover 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. The techniques you learn in CS188x 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. SYLLABUS Introduction o Overview o Agents: Perception, Decisions, and Actuation Search and Planning o Uninformed Search (Depth‐First, Breadth‐First, Uniform‐Cost) o Informed Search (A*, Greedy Search) o Heuristics and Optimality Constraint Satisfaction Problems o Backtracking Search o Constraint Propagation (Arc Consistency) o Exploiting Graph Structure Game Trees and Tree‐Structured Computation o Minimax, Expectimax, Combinations o Evaluation Functions and Approximations o Alpha‐Beta Pruning Decision Theory o Preferences, Rationality, and Utilities o Maximum Expected Utility Markov Decision Processes o Policies, Rewards, and Values o Value Iteration o Policy Iteration Reinforcement Learning o TD/Q Learning o Exploration o Approximation PREREQUISITES Programming o Object‐Oriented Programming o Recursion o Python or ability to learn Python quickly (mini‐tutorial provided) Data Structures o Lists vs Sets (Arrays, Hashtables) o Queuing (Stacks, Queues, Priority Queues) o Trees vs Graphs (Traversal, Backpointers) Math o Probability, Random Variables, and Expectations (Discrete) o Basic Asymptotic Complexity (Big‐O) o Basic Counting (Combinations and Permutations) This course requires both programming and math background. Required programming experience is at the level of a first course for CS majors. The language used will be Python—which in our experience students with programming experience pick up fairly quickly. Your best assessment on whether your programming background is sufficient is Project 1, which will go out in the first week, in which you will program an agent to intelligently navigate a maze. Math background: a first course in probability at the undergraduate level. We will post a self-assessment test in the first week.
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