COGS 118A: Natural Computation I (Winter 2012)

MWF 11a-1150a at York 3050a
Section: fri 1-150p HSS1106A

Course website:


Pradeep Shenoy (Dr.)

Office Hours: W 2-4 in CSB 157

Dario Gutierrez (TA)

Office Hours: Th 330-430 in CSB115

Course Description

This is an introductory course in Bayesian statistical methods for modeling natural computation. It will cover topics such as density estimation, linear and logistic regression, Bayesian networks, hidden Markov models, & reinforcement learning.  Students will have the opportunity to implement these algorithms in programming exercises, as well as derive some of their properties via mathematical proofs.  In addition, the course will touch upon applications of these algorithms in cognitive modeling and data analysis.

Pre-reqs: Cogs 109, Math 20E,F and Math 180A, or equivalent exposure to programming, linear algebra, calculus and introductory probability theory.



book cover

Additional Readings

Probability Theory (E. T. Jaynes, available online)
Information Theory Inference, and Learning (David J.C. Mackay, available online)

Course Details and Policies

Office hours: Wednesdays 2-4p CSB 157
Programming labs: You may use CSB115, click here for access codes.

Grading: Assignments 35%: late penalty 10%/day. Exams (30%). Final project (35%).

UCSD policy on integrity of scholarship: You must write your own code and homework assignments. You may discuss with other students.

Course Schedule

Week Date Topic Readings Assignments Notes
1 Mon Jan 09 Introduction (slides)      
Wed Jan 11 Probability review Ch 1.2-1.24, Linear Algebra Review     
Fri Jan 13 Density estimation Ch 2.1-2.2    
2 Mon Jan 16 No class (MLK Jr Day)   Assignment 1 (Assignment 1 due 01/23)
Wed Jan 18 Gaussian Distribution Ch 2.3.4-2.3.6    
Fri Jan 20 Cognitive science applications (slides) Kording & Wolpert, 2004    
3 Mon Jan 23 Linear regression Ch 3-3.1.4 Assignment 2 (Assignment 2 due 01/30)
Wed Jan 25 Linear regression (slides)      
Fri Jan 27 Bias-variance, Bayesian linear regression Ch 3.2 - 3.3    
4 Mon Jan 30 Talk by Andrew Ng      
Wed Feb 01 Bias/variance, Bayesian regression Ch 3.2-3.4 Assignment 3 (data) (code) (Assignment 3 due 02/08) (A1 solutions)
Fri Feb 03 Classification, Fisher's linear discriminant Ch 4.1-4.14    
5 Mon Feb 06 Fisher's discriminant, Probabilistic generative models Ch 4.1.4, 4.2    
Wed Feb 08 Logistic regression, IRLS, Bayesian Logistic regression Ch 4.3, 4.5    
Fri Feb 10 Bayesian networks, conditional independence Ch 8.1, 8.2 Assignment 4 (data) (code) (Assignment 4 due 02/22)
6 Mon Feb 13 Bayesian network examples   Project suggestions  
Wed Feb 15 Bayesian Networks: learning & inference (slides by davies & moore)    
Fri Feb 17 Cognitive modeling: perceptual decision-making, conflict resolution (slides) Gold & Shadlen 2007, Yu et al., 2009    
7 Mon Feb 20 No class (Presidents' day)      
Wed Feb 22 Linear Gaussian models, linear dynamical systems (kalman filter) (tutorial by Welch & Bishop)    
Fri Feb 24 Kalman filter, cognitive modeling: timescales in motor learning and adaptation (slides) Kording et al., 2007   A2 solutions
A3 solutions

8 Mon Feb 27 Hidden Markov models: inference (tutorial by L. Rabiner)    
Wed Feb 29 Hidden Markov models: inference and learning   Assignment 5 (Assignment 5 due mar 7)
Fri Mar 02 Markov decision processes (Survey by Kaelbling et al) (additional slides)    
9 Mon Mar 05 MDPs, reinforcement learning RL simulator    
Wed Mar 07 RL and the brain (slides) Glaescher et al., 2010, Daw et al., 2005    
Fri Mar 09 Rational models for inhibitory control (slides)      
10 Mon Mar 12 Exam review      
Wed Mar 14      
Fri Mar 16 Project presentations (Regular class time)     Sample Exam Questions
Fri Mar 16 Project presentations (sections)     Sample exam solutions