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| Lecturers | Tomi Janhunen and Jussi Rintanen |
| TA | |
| Workload | Lectures 20h, exercise sessions 20h, independent work 90h, examination 3h |
| Learning Outcomes | Artificial intelligence (AI) is about making computers automating complex tasks that have earlier been only possible for intelligent human beings, including thinking and reasoning, problem solving, learning, and decision making. The goal of this course is to give an in-depth introduction to AI methodology. Having completed the course, you have gained a comprehensive overview of AI and understand its fundamental principles. You have acquired skills needed for solving real-world problems through intelligent software technologies. |
| Content | The course presents a range of central AI techniques and provides the students with an extensive toolbox for solving problems in practice. Core AI techniques that show up in a broad range of applications include combinatory search methods, automated reasoning with logic and constraints, automated decision-making and planning, reinforcement learning, and supervised learning. |
Jan 10 1. Introduction to AI, Course organization
Jan 17 2. Search methods (see the points of interestfor the material presented at the lecture)
Jan 24 3. Logical reasoning (with points of interest as above)
Jan 31 4. Probabilistic reasoning (with points of interest as above)
Feb 7 5. Decision making under uncertainty and imperfect information
Feb 14 6. Applications via logic programming (with points of interest as above)
Feb 28 7. Multi-agent decision making and games
Mar 7 8. Supervised learning, Reinforcement learning
Mar 14 9. Data mining (with points of interest as above)
Mar 21 10. Applications and Summary