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

Final exam will be Tuesday December 7th

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)

Date | Lecture Topic | Slides | Reading | Week's Section Topic | Homework Notes | |
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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 |
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Wed Oct 27 |
cont'd from above |
pca2dexample.m faceexample09.m viewcolumn.m |
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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 |
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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 |
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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 |
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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) |
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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 |