Connectionists: Computational Cognition Cheat Sheets

[Robert Jacobs]

COMPUTATIONAL COGNITION CHEAT SHEETS

Over the years, our lab has written several notes (referred to as 
"Computational Cognition Cheat Sheets") providing brief introductions to 
computational methods that are often useful in the study of human 
cognition. Many students and faculty have told us that these notes are 
extremely useful, both for self-study and classroom teaching.

These notes are available from the following web page:

http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheets.html

There are currently 22 notes available on the following topics:

Backpropagation Algorithm
Bayesian Estimation
Bayesian Inference: Gibbs Sampling
Bayesian Inference: Metropolis-Hastings Sampling
Bayesian Inference: Particle Filtering
Bayesian Statistics: Beta-Binomial Model
Bayesian Statistics: Dirichlet Processes
Bayesian Statistics: Indian Buffet Process
Bayesian Statistics: Normal-Normal Model
Conditional Independence, Dependency-Separation, and Bayesian Networks
Factor Analysis
Hidden Markov Models
K-Means Algorithm for Clustering
Maximum Likelihood Estimation
Mixture Models
Mixtures-of-Experts
Optimal Linear Cue Combination
Principal Components Analysis
Principal Components Analysis and Unsupervised Hebbian Learning
Reinforcement Learning: Model-based
Reinforcement Learning: Model-free
Sensory Integration and Kalman Filtering

-- 
Robert Jacobs
Department of Brain and Cognitive Sciences
University of Rochester
Rochester, NY 14627-0268
email: robbie at bcs.rochester.edu
phone: 585-275-0753
web: http://www.bcs.rochester.edu/people/robbie/jacobslab/people.html