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CS188.1xArtificial Intelligence BerkeleyX - Acsu Buffalo


									CS188.1x: Artificial Intelligence                                             BerkeleyX


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.

       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 
       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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