Course Content:

The content of this course is independent of the content of the course machine learning. Both courses count as core courses for the Computational Intelligence Catalogue, and are part of the course catalogue for Doctoral Students.

This course presents the most successful methods for designing systems that learn autonomously, i.e., without a supervisor who tells the system at every trial what the ''right'' answer or action would have been. It is not surprising that many currently existing methods for autonomous learning are inspired by learning processes in biological organism, since most of their learning has to take place without a supervisor.
Also the most successful learning algorithms in real-world robots are reinforcement learning algorithms,

The course will present the best currently existing mathematical models and algorithmic solutions for autonomous learning, including recent work from Google Deep mind on learning to win in Atari games, and in the game Go.

We will start with Genetic Algorithms that mimick learning on the time-scale of evolution, and also discuss Simulated Annealing as an alternative. Then we will present learning strategies for learning to act in an unknown environment, with the goal to maximize rewards (Reinforcement Learning). For the more recent results from Google Deep mind we will discuss the combination of Deep Learning with Reinforcement Learning.