| Instructors: | Mike Landy & Eero Simoncelli |
| Teaching Assistants: |
Perri Katzman (perri AT nyu DOT edu), Nikhil Parasarathy (np1742 AT nyu DOT edu), Tina Voelcker (tinavoelcker AT gmail DOT com) |
| Time: |
Lectures: Tuesday/Thursday, 10:00-12:00 Labs: selected Fridays, 9:30-12:00 |
| Location: |
Lectures: Hebrew Union College (1 West 4th Street), rm CR-3 (lower level) Labs: Meyer Hall, rm 551 |
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, classification, linear discriminants, clustering, simple 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!
Announcements: We use Piazza for online class announcements, questions, and discussion: https://piazza.com/nyu/fall2018/psychga2211neurlga2207/home . Rather than emailing the instructors or TAs, we encourage you to post your questions/comments there, where they can be discussed and/or answered by any of us or your fellow classmates.
Schedule: (Note: labs in green, content will appear incrementally, for a preview see last year's course page)
| Date | Topic (Videos) | Handouts | Homework |
|---|---|---|---|
| Tue, Sep 4 |
Introduction to the course
Linear algebra I: vectors, vector spaces, inner products (video) |
Handout: Course description (pdf)
Slides: Linear algebra (pdf) Notes: Linear algebra review (pdf) |
|
| Thu, Sep 6 |
Linear algebra II: projection, coordinate systems, linear systems
(video) |
Homework i (optional matlab practice, due 9/14) (pdf) | |
| Fri, Sep 7 | Matlab I: Environment, basic data types and operations, plotting |
Matlab installation (txt)
Matlab primer (pdf) Lab script (m) |
Homework 1 (due 9/23) -
(pdf)
mtxExamples (mat) |
| Tue, Sep 11 |
Linear Algebra III: orthogonal/diagonal matrices, geometry
(video) |
||
| Thu, Sep 13 |
Linear Algebra IV: SVD, null and range spaces, inverses
(video) |
||
| Fri, Sep 14 | Matlab II: Scripts, control structures | Lab2 files (folder) | |
| Tue, Sep 18 | Matlab III: Exploring SVD | Slides: review of SVD (pdf) | |
| Thu, Sep 20 | Linear Algebra example: Trichromacy
(video) |
||
| Fri, Sep 21 | Regression I: Least squares and multiple regression
(video) |
||
| Tue, Sep 25 | Regression II: weighting, outliers, overfitting
(video) |
Notes: Least Squares (pdf) |
Homework 2 (due Oct 11) -
(pdf)
hw2-files (zip) |
| Thu, Sep 27 | Regression III: incorporating linear/quadratic constraints
(video) |
Slides: Least Squares (pdf) | |
| Fri, Sep 28 | [no lab - Psychology mini-convention] | ||
| Tue, Oct 2 |
Regression IV: TLS regression, PCA, eigenvalues/eigenvectors
(video) |
||
| Thu, Oct 4 |
LinSys I: LSI Systems, convolution
(video) |
||
| Fri, Oct 5 | Lab: Regression/PCA | Lab slides: regression (pdf) | |
| Tue, Oct 9 | [no class - NYU classes meet on a Monday schedule] | ||
| Thu, Oct 11 |
LinSys II: Sinusoids, Discrete Fourier transforms
(video, from 2017) |
||
| Fri, Oct 12 | Lab: Convolution | Materials (zip) |
Homework 3 (due Oct 28) -
(pdf)
unknownSystems (zip) hrfDeconv (mat) myMeasurements (mat) |
| Tue, Oct 16 |
LinSys III: Fourier Transforms and LSI Systems
(video) |
Slides: LSI systems (pdf) | |
| Thu, Oct 18 |
LinSys IV: Fourier examples, indexing/plotting, sampling
(video) |
Notes: LSI systems (pdf) | |
| Fri, Oct 19 | Lab: Fourier Transforms | Materials (zip) | |
| Tue, Oct 23 | LinSys example: Sound and the cochlea
(video) |
Slides: Auditory system (pdf) | |
| Thu, Oct 25 | Prob & Stats intro: Descriptive statistics
(video) |
||
| Fri, Oct 26 | [no lab] | ||
| Tue, Oct 30 |
Prob I: distributions, expectations, moments
(video) |
Homework 4 (due Nov 13) - (pdf) | |
| Thu, Nov 1 |
Prob II: joint distributions, marginals, conditionals, Bayes Rule,
independence
(video) |
||
| Fri, Nov 2 | [no lab - SfN meeting] | ||
| Tue, Nov 6 | [no class - SfN meeting / election day!] | ||
| Thu, Nov 8 | Stats I: Averages: 1/N convergence, CLT, standard
error
(video) |
Slides: Probability (pdf) | |
| Fri, Nov 9 | Lab: Probability/Sampling | Lab7 materials (zip) | |
| Tue, Nov 13 | Stats II: 1D estimation, MLE, MAP, BLS, bias/variance tradeoff
(video) |
||
| Thu, Nov 15 | Stats III: Standard error, p-values, t-test,
permutation tests, bootstrapping
(video) |
Handout: The Bootstrap | |
| Fri, Nov 16 | Lab: Statistical Inference | Lab8 script file (m) | Homework 5 (due Nov 30) - (pdf) |
| Tue, Nov 20 | Stats, extended example: signal detection theory
(video) |
||
| Thu, Nov 22 | [no class - Thanksgiving] | Fri, Nov 23 | [no lab - Thanksgiving] |
| Tue, Nov 27 | Stats: multi-dimensional classification
(video) |
||
| Thu, Nov 29 | Stats: Regression to the mean, correlation
(video) |
||
| Fri, Nov 30 | Lab: Simulations, bootstrapping, cross-validation | ||
| Tue, Dec 4 | Stats example: Fitting an LNP model to neural data
(video) |
Homework 6 (due Dec 18) - (pdf)
psychopathy (mat) |
|
| Thu, Dec 6 |
Stats: Optimization, overfitting, model selection, cross-validation
(video) |
||
| Fri, Dec 7 | [no lab] | ||
| Tue, Dec 11 | Stats: Regularization (ridge regression, LASSO)
(video) |
||
| Thu, Dec 13 | Stats: Clustering, Hessians.
(video) |
||
| Fri, Dec 14 | Lab: multi-dimensional decision/classification |
Lab script file (m) fisherData ( mat) Slides: Statistical Inference (pdf) |
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