Course Content
- Introduction to Artificial Intelligence
- State spaces and problem solving through search
- The search algorithms DFS, BFS, UCS, and iterative deepening
- Informed search. Greedy search algorithms and the A* algorithm
- Adversarial search. The Minimax and Expectimax algorithms and their variants
- Agent utility in search problems
- Non-deterministic search problems and Markov decision processes
- The value iteration, policy evaluation, and policy iteration algorithms
- Reinforcement learning. Model-based and model-free learning
- The temporal-difference learning (TD learning) algorithm and the Q-learning algorithm
- Constraint satisfaction problems
- Backtracking search with arc consistency
- Local search algorithms and their variants
- Introduction to Machine Learning
Learning Outcomes
The aim of the course is to introduce basic concepts of Artificial Intelligence and to develop an understanding of selected methods for solution search, adversarial search, reinforcement learning, and constraint satisfaction problems. An introduction machine learning and basic algorithms, such as linear regression, is also included in the course.
Upon successful completion of the course, the student will be able to understand and apply representative methods from each category in order to solve practical artificial intelligence problems.
