Modeling and Data Analysis - COGS 109 Fall 2010

# Modeling and Data Analysis - COGS 109 Fall 2010

## See note on Nov 29 entry about HW5

All questions should be posted to the

### Students in need of extra help, may attend extra sections as needed (space permitting)

Cheating will not be tolerated: University Academic Dishonesty Policy

### Current Grades (including Midterm) will be available on webct

Midterm1 Stats from 2008 Midterm2 Stats from 2008

### 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

Section info to go here (All Sections in CSB 115)

Final exam will be Tuesday December 7th

## Useful References

Online Matlab Tutorials

## Course Goals

This is a relatively 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
-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 2010B
Date Lecture Topic Slides Reading Week's Section Topic Homework Notes
Fri
Sept 24
Introduction to COGS 109 Slides here NO Sections OPEN MATLAB and type demo and explore some of the getting started videos
Mon
Sept 27
Intro to Linear Algebra Notes here free online linear algebra notes Math Review/Help
Weds
Sept 29
Intro to Matlab Notes 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
Fri
Oct 1
Matlab Functions and graphing Notes here pp 41-56, pp 22-26, Appendix E Homework 1 functions and derivatives HW1 Matrix and Vector Algebra
Mon
Oct 4
Matlab Functions and graphing see notes above pp 41-56, pp 22-26, Appendix E
Wed
Oct 6
Probability refresher, Bayes rule Notes here
Fri
Oct 8
Using the Matlab programming environment - Josh Lewis
Mon
Oct 11
Data Visualization notes here There is a fair amount of online reading embedded in the notes above
Wed
Oct 13
BCI's Neurosky guest speaker
Fri
Oct 15
Clustering, Kmeans Slides here HW2 NOW POSTED
Mon
Oct 18
stats issues hypothesis testing comparetests.m pp 127-131 (text)
Wed
Oct 20
filtering filtering filterdemo.m
Fri
Oct 22
filtering cont'd
Mon
Oct 25
PCA HW2 DUE at beginning of class -- Bring your HW to class PCA slides now here
supplemental covariance slides (called from above slides
Wed
Oct 27
cont'd from above pca2dexample.m
faceexample09.m viewcolumn.m
Fri
Oct 29
REVIEW FOR MIDTERM midterm 1 supplemental study notes
Mon
Nov 1
Midterm 1
Wed
Nov 3
PCA cont'd faceexample09.m
hw3bdata.mat
pcaexample2d.m
viewcolumn.m
eigsort.m
Fri
Nov 5
Cont'd from above HW3 now available -- see advice notes and helper functions (eigsort.m, viewcolumn.m) on wiki (helper functions also above)
Mon
Nov 8
Linear Regression, overfitting, non-linear function fitting Notes now here
predyval.m (used in the notes)
linregress.m (extracted code from notes)
linregresslargerexample.m (extracted code from notes)
leaveoutcode.m (extracted code from notes)
o
Wed
Nov 10
Nonlinear function fitting Updated notes now here
myfitdemoslow.m
myfitfunslow.m
myfitdemo.m
myfitfun.m
Fri
Nov 12
Introduction to Neural Networks Notes here (minus graphs drawn in class)
Mon
Nov 15
Gradient Descent Notes now here (minus graphs and derivations in class) HW4 now available
Wed
Nov 17
Multi-layer perceptrons Notes to be available (minus graphs and derivations in class)
neuralnetfit.m
neuralfitfun.m
neuralnetfit2.m
neuralfitfun2.m
Fri
Nov 19
Training Networks in Matlab Notes now here
xorbplay.m
xorbplayver71.m
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 READ NOW Concentrate on Intro, Fundamentals
Mon
Nov 22
Training issues in Neural Networks Notes now here
Wed
Nov 24
Review for Midterm 2 Sample midterm 2 questions here (updated 11/27 with PCA questions at top)
nnet.jpg (used in sample midterm)
nnet4.jpg (used in sample midterm)
Sample MT2 solutions now posted (11/27) NOTE REVIEW NOTES FOR WHOLE COURSE ARE BELOW -- FOR MIDTERM 2 KNOW PCA through PERCEPTRONS (see delineation in the review notes)
Mon
Nov 29
Neural Networks and Review for MT2 cont'd HW5 (Do not panic -- this should be considered a study aid for the final exam (not MT2) - solutions will be provided before it is due at the final exam)
Wed
Dec 1
Midterm 2
Fri
Dec 3
Last Class - Review review notes now here