The course will introduce the basic ideas and techniques underlying the design and learning 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:
    Search/Optimization Techniques
    Constraint Satisfaction Problems
    Bayes Decision Theory / Classifiers
    Markov Decision Processes
    Reinforcement Learning
    Explainable AI

Format: Written exam at the end of the semester.

 

• 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.