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This program requires experience with linear algebra, statistics, and Python (including object-oriented programming).
Use constraint propagation and search to build an agent that reasons like a human would to efficiently solve any Sudoku puzzle.
Build agents that can reason to achieve their goals using search and symbolic logic—like the NASA Mars rovers.
Extend classical search to adversarial domains, to build agents that make good decisions without any human intervention—such as the DeepMind AlphaGo agent.
Model real-world uncertainty through probability to perform pattern recognition.
Research Director, Google
Peter Norvig is a Director of Research at Google and is co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the field.
As the founder and president of Udacity, Sebastian’s mission is to democratize education. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more.
Professor of Computer Science, Georgia Tech
Thad Starner is the director of the Contextual Computing Group (CCG) at Georgia Tech and is also the longest-serving Technical Lead/Manager on Google's Glass project.
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function Hill-Climbing(problem) returns a Statecurrent <- Make-Node(problem.Initial-State) loop do neighbor <- a highest-valued successor of current if neighbor.value ≤ current.value then return current.state current <- neighbor</pre>