Artificial Intelligence

This course aims at providing insight in the fundamental concepts of the theory and applications in the broad Artificial Intelligence discipline. An overview of the most commonly used methods and models is presented, of which a number are treated in depth. Especially, focus is put on the topic Machine Learning (particularly neural networks and deep learning) and data driven model building, including Bayesian learning. We start from the theory of supervised learning with basic linear regression and linear classification problems gradually building towards more complex supervised learning tasks. Then we turn to unsupervised learning, and particularly dimensionality reduction and clustering. The course also places machine learning into a broader perspective of Artificial Intelligence, where we also investigate problem solving agents (search and game playing), decision problems (including Markov decision processes), and basics of reinforcement learning.

In this way, by the end of the course, all the concepts and topics covered are brought together to tackle the most challenging problems of autonomous decision-making and rational action under uncertainty.

The theoretical classes are complemented by exercises, including computer-based exercises and demonstration sessions