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Bayesian Statistics and Machine Learning in Neuroscience

Overview

This course consists of lectures and exercises about

Foundations of Probability,
Bayesian Reasoning and Networks,
Stochastic Processes,
Neural Networks and Deep Learning.

Literature suggestions for the beginner

On 'Why':
Larry Bretthorst and Edward Jaynes (2003): Probability Theory, the Logic of Science. Cambridge Univ. Press
Joseph Halpern (2003): Reasoning about Uncertainty. MIT Press.

On 'Howto':
Christopher Bishop (2006): Pattern Recognition and Machine Learning. Springer.
Carl Rasmussen and Christopher Willams (2005): Gaussian Processes

Even more in-depth 'Howto':
Daphne Koller and Nir Friedmann (2009): Probabilistic Graphical Models. MIT Press.
Kevin Murphy (2012): Machine Learning, a Probabilistic Perspective. MIT Press.
David Barber (2012): Bayesian Reasoning and Machine Learning. Cambridge Univ. Press.

Causality:
Judea Pearl (2009): Causality.

More Information

Regular cycle: winter semester

Eligible: students of Psychology, and Cognitive and Integrative Systems Neuroscience (M.Sc.)