Artificial Intelligence (Course)
Course Description
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
Suggested Reading
- Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (http://aima.cs.berkeley.edu/)
Prerequisites:
Basic knowledge in Mathematics and Algorithms & Data structures.