Short description:

The course is an introduction to the area of Artificial Intelligence and will introduce the basic ideas and techniques underlying the design of intelligent machines. By the end of this course, you will have learned how to build autonomous (software) agents that efficiently make decisions in fully informed, partially observable and adversarial settings as well as how to optimize actions in uncertain sequential decision making environments to maximize expected reward.

Syllabus:

• Informed search
• Uninformed search
• Adversarial search
• Local search and Optimization
• Constraint Satisfaction Problems
• Markov Decision Processes
• Reinforcement Learning

• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (http://aima.cs.berkeley.edu/)
• Richard Sutton and Andrew Barto, Reinforcement Learning: An Introduction (https://mitpress.mit.edu/books/reinforcement-learning-second-edition)

Prerequisites:

Basic knowledge in Mathematics and Algorithms & Datastructures.