ML A versus ML B:

Each of these two courses is offered every second year. You can take ML B without having taken before ML A, since their content is independent and complementary to each other (see course descriptions on Both courses count as core courses for the Computational Intelligence Catalogue, and belong to the course catalogue for Doctoral Students.

Course Content of ML B:

This course presents the most promising ideas and 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 most currently existing methods for autonomous learning are inspired by various learning capabilities of biological organism, since most of their learning has to take place without a supervisor. One long-range goal of machine learning and artificial intelligence is to design artificial agents (e.g. robots) that are able to configure themselves for a given range of tasks, to learn to carry out the right action in a given situation in order to minimize a certain long-range cost (or maximize some external reward), and to acquire cognitive capabilities that enable them to detect on their own which features of their environment are relevant for them, to discover causal relationships between relevant phenomena, and to discover rules and simple theories that explain these phenomena on a more abstract level. The course will present the best currently existing mathematical models and algorithmic solutions for solving these problems. We will start with Genetic Algorithm that mimick learning on the time-scale of evolution, present the main concepts and results for learning strategies for acting within an unknown environment that maximize external rewards (Reinforcement Learning), and we present recent results from Cognitive Science that explain through precise algorithmic models how humans can learn new concepts from very few examples, and how infants can discover salient causal relationships in their environment, and form simple theories. These results from Cognitive Science that were discovered during the last decade at MIT by Josh Tenenbaum, at Berkeley by Tom Griffiths, and many others, use the framework of probabilistic inference to explain human reasoning, and because of their mathematical precision these methods can immediately be ported to artificial computing systems (which our Institute is going to carry out in the new EU-project BRAINSCALES that begins on January 2011). In particular we will discuss how probabilistic inference provides more flexible methods for learning motor control strategies in robotics, and enables all artificial agents to learn faster by learning simultaneously on several levels of abstraction . No prior knowledge will be assumed on concepts and methods for probabilistic inference (the treatment in this course will be complementary to that in ML A). We believe that is is very important that all our master students become familiar with the basic concepts and methods of probabilistic inference, since this framework is emerging as a new standard approach in many areas of artificial intelligence, robot control, signal processing, cognitive science, and computational neuroscience.