In Foundation of Artificial Intelligence you will learn the basics of how computer try to mimic intelligence or human behavior.
After a brief, philosophical introduction to various kinds of intelligence we will focus on agents such as “robotic vacuum cleaner” which work autonomously to achieve goals.
Besides formal details such as agent architectures and environment specifications we will talk in greater detail how information is handled and decision are made from an algorithmic viewpoint. This entails for one finding information in graphs (uninformed search, informed search, local search), but also how an agent deals with uncertainty and not available information.
We will furthermore provide an introduction to the field of machine learning where we discuss various techniques to let a machine recognize objects in the real world (classification, clustering, regression), their prerequisites and how to evaluate them.
- History of AI
- Definition of Intelligence
- Agent Architecture
- Properties of Environments
- Uninformed Search (BFS, DFS)
- Informed Search (Greedy, A*)
- Local Search (Genetic Algorithms)
- Uncertainty / Probabilistic Models
- Machine Learning
- Classification (Naive Bayes, Decision Trees)
- Applications of AI