Content

• Part 1 - Search strategies: As an introduction and a prerequisite for most of the principles of artificial intelligence search strategies are introduced and explained. We will introduce uninformed, informed, local search, adversial search as well as heuristic search. The concept of agents will be presented.
• Part 2 - Learning and reasoning: Revision of the foundations of mathematical logic and probability. Principles of machine learning (supervised and unsupervised) are introduced. An introduction to fuzzy logic is also included.
• Part 3 - Applications of artificial intelligence: Typical applications in the fields or robotics, machine vision, and industrial image and data processing are identified. Ethical issues and risks of the development of artificial intelligence are discussed.

Qualification-goals / Competencies

• Ability to solve handle scope-oriented tutorials with a mathematical background
• Understanding the benefits and disadvantages of the different search and problem solving techniques
• Be able to choose and apply independently appropriate algorithms for search and learning issues
• Gained an insight into the complex development of systems with artificial intelligence and the distinction of its various forms
• Understanding of the risks and possible technological consequences of the development of systems with strong AI