Computational Neuroeconomics and Neuroscience
Time and location
|Wed, 15:00-16:30||Room: BLU-E-003|
This seminar covers a broad variety of mathematical and statistical methods used in contemporary computational neuroscience and neuroeconomics. This semester is devoted to machine learning techniques, many of which are promising tools for neural data analysis and the modeling of human learning and decision making. This semester we will cover a textbook entitled Pattern Recognition and Machine Learning (2007, corr. 2nd printing ed.) by Christopher M. Bishop. Each week a course participant will present one chapter of the book, which will be followed by general discussion. Most chapters contain more material than can be presented and discussed within 90 minutes, so presenters are encouraged to focus on a selection of ideas that can be presented in 45-60 minutes in order to allow for questions and discussion.
If you wish to attend this course, please send an e-mail with your name, address, telephone number, and student ID number (Matrikelnummer) to Tamara Herz.
Please send your suggestions for the course schedule or any feedback to Lars Kasper.
|22-09-2010||Probability, Decision, and Information Theory||1|
|13-10-2010||Density Estimation, Bayesian Inference||2|
|20-10-2010||Linear Models for Regression||3|
Winterthurerstrasse WIH 03
|Linear Models for Classification||4|
|03-11-2010||Kernel Methods I: Gaussian Processes||6|
|10-11-2010||Kernel Methods II: SVM and RVM||7|
|17-11-2010||Probabilistic Graphical Models||8|
|24-11-2010||Mixture Models and EM||9|
|01-12-2010||Approximate Inference I:
|08-12-2010||Approximate Inference II:
|15-12-2010||Inference on Continuous Latent Variables:
PCA, Probabilistic PCA, ICA
|22-12-2010||Sequential Data: Hidden Markov Models,
Linear Dynamical Systems