Artificial Intelligence

This course gives an introduction to the fundamentals of Artificial Intelligence (AI) including robotics and natural language processing, exploring both the historical roots and the latest applications. You will learn how to implement and apply classical AI algorithms for knowledge representation, planning, searching, and game-playing. The course focuses on symbolic artificial intelligence with techniques based on discrete mathematics and logics. Non-symbolic approaches common in machine learning and deep learning are briefly introduced and discussed in relation to symbolic AI. **Module 1, theory, 4.5 credits.** The module introduces classical AI. It covers some historical background, fundamental problems, and modern-day applications. The course discusses problems such as the frame problem, the Turing test, the Chinese room argument, and various problem-solving techniques, including heuristic search algorithms and AI in games. Additionally, the module addresses AI paradigms such as knowledge representation, logic programming, and robotics with an emphasis on sensors and actuators. It covers different agent paradigms, classical planning methods, and machine learning algorithms such as k-NN and deep learning. The module also explores natural language processing (NLP), Large Language Models (LLMs), and Explainable AI. Ethical considerations and responsible AI use are emphasized, addressing biases and ensuring transparency and accountability. **Module 2, practice, 3 credits.** In this module, some of the theories, methods, and concepts treated in the theory module are put into practice.