Modeling and Data Analysis - COGS 109 Fall 2008

Modeling and Data Analysis - COGS 109 Fall 2008

York 2622 : Tuesday and Thursday 9:30-10:50a

HW 2 now posted below -- Due Oct 24th (start early)

All questions should be posted to the

course wiki.

Textbooks are now in the bookstore

Students with one-time conflicts (e.g. sickness, holidays) may attend other sections as needed. Please try to target the less full sections.

Cheating will not be tolerated: University Academic Dishonesty Policy

Current Grades (for people with signed waiver) will go here

Professor: Virginia de Sa (SSRB/CRB (not CSB) 2nd floor phone 2-5095)

TAs: Josh Lewis Vicente Malave Doug Yovanovich

IA: Jacob Bergsma

Office hours/locations are listed on the course wiki

Required Text: Matlab for Engineers Explained (Gustafsson and Bergman) -- Amazon has for $32.54)


Grading Scheme: Midterms, Final Exam, approx 5 programming assignments (30%). We will downweight the midterms if you do better on the final. IF you hand in all assignments, we will remove the lowest grade. But you must hand in all assignments for this privilege.


For access code to CSB 115

For your account login info

All Sections in ERCA 117

Section info
628229 / A05 M / 1300-1350 ERCA 117 27 / DY, JB
628230 / A06 M / 1400-1450 ERCA 117 27 / DY, JB
628225 / A01 T / 1100-1150 ERCA 117 22 / VM
628226 / A02 T / 1200-1250 ERCA 117 11 / VM
628227 / A03 W / 1100-1150 ERCA 117 27 / JL, JB
628228 / A04 W / 1200-1250 ERCA 117 26 / JL

Final exam will be Dec 11 at 8AM

Useful References

Online Matlab Tutorials

Course Goals

This is a new course designed to be both an
A) ``end of the road computation course'' for non-Computational majors
B) an introduction to serious Computational Methods for those taking the 118(A/B) courses.

Doing both well involves a somewhat difficult compromise but there are several aspects to both
-Improve your programming skills
-Introduce you to the wonders of Matlab
-Give you a flavor for Cognitive Science data analysis and modeling applications (many of which will explained in mathematical depth in 118)
Tentative Course Schedule Fall 2008
Date Lecture Topic Slides Reading Week's Section Topic HomeWork Assignments
Thu
Sept 25 		Introduction Slides (now here) NO Sections
Tue
Sept 30 		Intro to Matlab Notes now here Preface, pp1-8, 12-15, 45-52 (Matlab book), Look over appendices Derivatives (helpful for section and homework) , nice online tutorial , Look over what is available under the Matlab tutorials link above Math Review/Help
Thu
Oct 2 		Matlab Functions and graphing Notes now here pp 41-56, pp 22-26, Appendix E Homework 1 now posted
Tue
Oct 7 		Data visualization, graphing Notes now posted
Thu
Oct 9 		Clustering and PCA Slides now here
Tue
Oct 14 		PCA revisited pca2dexample.m pca3dexample.m faceexample.m hw4data.mat viewcolumn.m prepcav.m
Thu
Oct 16 		standard stats, multiple comparison issues, filtering filtering.txt hypothesis tests slides multinorm.m multicompar.m pp 127-131 (text) HW 2 due Oct 24 (now here) hw2data.mat viewcolumn.m prepcav.m
Tue
Oct 21 		Linear Regression Notes 108-110 (all of Chap 18 if you can) Matlab, linear regression
Thu
Oct 23 		Overfitting, Non-linear function fitting Notes
Tue
Oct 28 		History of GOFAI Notes
Thu
Oct 30 		Midterm 1 Practice Midterm
Tue
Nov 4 		Introduction to Neural Networks Notes (minus graphs drawn in class)
Thu
Nov 6 		Hopfield Networks and application
Tue
Nov 11 		Veterans Day/Remembrance Day -no lecture
Thu
Nov 13 		Gradient Descent Notes (minus graphs and derivations in class)
Tue
Nov 18 		Multi-layer perceptrons NEW!!! Notes (minus graphs and derivations in class) Chapter 2 (Neural Network Toolbox 4.0 Release Notes) (available thru Matlab's help system or at http://www.mathworks.com/access/helpdesk/help/toolbox/nnet/rn/neural12.html
Thu
Nov 20 		Midterm 2 practice midterm practice midterm answers nnet.jpg midterm2 solution
Tue
Nov 25 		Training Networks in Matlab Notes Backpropagation section of the Neural Network Toolbox hDocumentation (available from the help system under Neural Network Toolbox) or online at http://www.mathworks.com/access/helpdesk/help/toolbox/nnet/backprop.html#backpropagation Concentrate on Intro, Fundamentals
Thu
Nov 27 		US THANKSGIVING -no lecture
Tue
Dec 2 		Training issues in Neural Networks Notes Backpropagation section of the Neural Network Toolbox hDocumentation (available from the help system under Neural Network Toolbox) or online at http://www.mathworks.com/access/helpdesk/help/toolbox/nnet/backprop.html#backpropagation Concentrate on Faster Training and Improving Generalization as well as Limitations and Cautions and Summary Homework
Thu
Dec 4 		Last Class - Review Review Notes