Form of learning:
Target group and prerequisites
The course presents a range of central AI techniques and provides the students with an extensive toolbox for solving problems in practice. For applications that require high degree of adaptation, specific techniques such as (deep) machine learning, reinforcement learning, and graphical models are included. These methods are instrumental for decision under uncertainty. For the purposes of knowledge representation and reasoning, different logical representations such as formulas and rules are covered. These representations establish the foundations for declarative problem solving and enable the use of state-of-the-art solver technology to search for solutions. The course also encourages students to combine the logical and machine learning perspectives when solving future problems.
Artificial intelligence (AI) tackles complex real-world problems, such as question answering, speech recognition, social network analysis, and task scheduling, with rigorous mathematical methods and tools. The goal of this course is to give an in-depth introduction to AI methodology while approaching the topic from the perspective of concrete application problems.
Having completed the course, you have
- gained a comprehensive overview of AI and understand its fundamental principles related to machine learning and logical reasoning.
- excellent premises for solving real-world problems with modern AI techniques and building intelligent systems by implementing such techniques.
The course material will be available on the course page.
No attendance on campus is necessary. All lecture recordings will be available online.
Mandatory weekly homework, including programming tasks.