Date  Topic 
Reading/Supp Material 
Homework 
Tues 1.19 
Preliminaries, overview. 


Thurs 1.21 
What is modeling? Compact and predictive descriptions of data.
Classification example. Nonlinear regression example: modeling as curvefitting. Slides 
Christopher Bishop: Neural Networks for Pattern Recognition, ch 1. 

Tues 1.26 
Overfitting and crossvalidation. Slides
Sample statistics and linear regression. Slides 


Thurs 1.28 
Timeseries: cross and autocorrelation. Slides 

Problem set 1 
Tues 2.02 
Cross and autocorrelation applied to spike train data. Slides 


Thurs 2.04 
Spiketriggered average (STA), relationship to crosscorrelation. Slides 
Dayan & Abbott, chapter 1. Bialek et al., Reading the neural code 
Problem set 2
c1p8.mat gridcell_halfmsbins.mat 
Tues 2.09 
STA continued. Slides 


Thur 2.11 
Convolution and the retina. Slides 

Problem set 3
c1p8.mat
generate_STAdata.m 
Tues 2.16 
Edgedetection in the retina. Slides
Introduction to WienerHopf filtering. Slides 


Thur 2.18 
Finding the leastsquares optimal kernel: WienerHopf equations. Linear regression, STA kernels as special cases. Matrix introduction. Slides 

Problem set 4
plant.mat
generate_STAdata.m 
Tues 2.23 
Basics of linear algebra I: matrices, vectors, sums, products. Slides 
Nice set of introductory notes on linear algebra by Z. Kolter. 

Thurs 2.25 
Basics of linear algebra II: vector spaces, linear independence, basis. 

Problem set 5 
Tues 3.01 
Basics of linear algebra III: rank, over and underdetermined problems. 


Thurs 3.03 
Pseudoinverse for solving over and underdetermined problems (linear regression revisited). 

No homework (midterm study). Problem set 15 solutions 

Tues 3.08 
Midterm exam (inclass, closedbook). 


Thur 3.10 
Changeofbasis, eigenvalues and eigenvectors, PCA. 


Tues 3.15 
No class spring break 


Thur 3.17 
No class spring break 


Tues 3.22 



Thur 3.24 



Tues 3.29 



Thur 3.31 



Tues 4.05 



Thur 4.07 



Tues 4.12 



Thur 4.14 



Tues 4.19 



Thur 4.21 



Tues 4.26 



Thur 4.28 



Tues 5.03 



Thur 5.05 
Final exam  in class. 









