Topics include: Linear algebra, least-squares and total-least-squares regression, eigen-analysis and PCA, linear shift-invariant systems, convolution, Fourier transforms, Nyquist sampling, basics of probability and statistics, hypothesis testing, model comparison, bootstrapping, estimation and decision theory, signal detection theory, models of neural spike generation, white noise (reverse-correlation) analysis.

Prerequisites: Algebra, trigonometry, and calculus. Some experience with matrix algebra and/or computer programming is helpful, but not required. The real prerequisites are an aptitude for logical and geometric reasoning, and a willingness to work hard!

We will be using Piazza for class discussion, which allows you to post questions or comments, and receive answers, on any aspect of the course. Our class page is: https://piazza.com/nyu/fall2016/psychga2211neurlga2207/home

Lecture & Lab Schedule    (content will appear incrementally...)

Resources (Electrons):

• Online matlab help at The MathWorks | Tutorial at the MathWorks | Intro video at MIT | Antonia Hamilton's Tutorial | at U. Utah | on reddit
• Richard Johnson's Matlab Style Handbook (lots of helpful tips, if a bit idiosyncratic)
• Linear algebra Appendix from PDP series, by Michael Jordan. (pdf)
• Online lecture videos from Gilbert Strang's course at MIT
• Todd Will's Interactive Intro to the SVD
• The Elements of Statistical Learning, Hastie, Tibshirani and Friedman - Excellent textbook on regression, decision/classification, clustering, and many advanced topics in data fitting and analysis. Available online (pdf)
• Convex Optimization, Boyd and Vandenberg - Excellent textbook on the formulation of, and algorithms for, optimization of convex functions. Available online (pdf)
• Thomas Minka's On-line Glossary of Statistical Pattern Recognition
• Wolfram Research World of Mathematics
• History of various topics in mathematics

Resources (Dead Trees):

• Matlab:
  Getting Started with MATLAB; A Quick Introduction for Scientists and Engineers, R. Pratap, Oxford U. Press, 2009.
  Matlab for Neuroscientists. An introduction to scientific computing in Matlab, P. Wallisch, M. Lusignan, M. Benayoun, T. Baker, A. Dickey & N. Hatsopoulos, Elsevier Press, 2008.
  Mastering MATLAB, B. L. Littlefield & D. C. Hanselman, Prentice-Hall, 2011.
• Linear algebra / Least squares:
  Linear Algebra and Its Applications, by Gilbert Strang. Academic Press, 1980.
• Linear (shift-invariant) Systems:
  Discrete-time Signal Processing, A. Oppenheim & R. Schafer. Prentice Hall, 1989.
  The Fourier transform and its applications, R. Bracewell, McGraw Hill Science, 1999.
  Fast Fourier transform and its applications, E. Brigham, Prentice Hall, 1988.
• Probability/Statistics:
  Statistics, Freedman, Pisani, Purves, Norton, 2007 (4th ed.)
  Mathematical statistics, J. E. Freund, Prentice Hall, 1992.
• Decision Theory:
  Biology: Elementary Signal Detection Theory, by Thomas D. Wickens. Oxford University Press, 2001.
  Signal Detection Theory and Psychophysics, by David Green & John Swets. Peninsula Publishing, 1988.
  Math: Statistical Decision Theory, by James O. Berger. Springer-Verlag, 1980.
  Chapter 2 of Pattern Classification, by Duda, Hart and Storck. Wiley, 2001.
• Bootstrap/Resampling:
  An Intoduction to the Bootstrap, by Bradley Efron and Robert Tibshirani. Chapman & Hall, 1998.
  Resampling Methods: A practical guide to data analysis, by Phillip Good. Birkhäuser, 1999.
• Spikes, Neural Coding, Reverse Correlation:
  Spikes: Exploring the Neural Code, by Fred Rieke, David Warland, Rob De Ruyter, & Bill Bialek. MIT Press, 1997.
• Computational/Theoretical Neuroscience:
  Theoretical Neuroscience , by Peter Dayan and Larry Abbott. MIT Press, 2001.