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SCHEDULE.md

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  • wed 8/29 - intro & syllabus

  • fri 8/31 - git & github

  • wed 9/5 - python & ipython

  • fri 9/7 - python

  • mon 9/10 - python (data types)

  • wed 9/12 - python (data types)

  • fri 9/14 - numpy - HOMEWORK 1 ASSIGNED

  • mon 9/17 - numpy

  • wed 9/19 - numpy

  • fri 9/21 - psth, spike triggered average - HOMEWORK 1 DUE - HOMEWORK 2 ASSIGNED

  • mon 9/24 - matplotlib

  • wed 9/26 - matplotlib

  • fri 9/28 - ipyvolume, pycortex - HOMEWORK 2 DUE - HOMEWORK 3 ASSIGNED

  • mon 10/1 - probability (bayes theorem, bernoulli, binomial, poisson)

  • wed 10/3 - probability (gaussians, central limit theorem)

  • fri 10/5 - descriptive statistics - HOMEWORK 3 DUE - HOMEWORK 4 ASSIGNED

  • mon 10/8 - multivariate gaussians

  • wed 10/10 - bootstrap

  • fri 10/12 - confidence intervals

  • mon 10/15 - hypothesis testing (p-value)

  • wed 10/17 - likelihood ratio test

  • fri 10/19 - permutation and bootstrap test - HOMEWORK 4 DUE - HOMEWORK 5 ASSIGNED

  • mon 10/22 - timeseries

  • wed 10/24 - fourier transform, spectrum

  • fri 10/26 - filtering

  • mon 10/29 - wavelets, spectrograms

  • wed 10/31 - autoregression

  • fri 11/2 - (re)sampling - HOMEWORK 5 DUE - HOMEWORK 6 ASSIGNED

  • mon 11/5 - linear regression

  • wed 11/7 - linear regression

  • fri 11/9 - regularization

  • mon 11/12 - logistic regression

  • wed 11/14 - support vector machines

  • fri 11/16 - principal components analysis - HOMEWORK 6 DUE - HOMEWORK 7 ASSIGNED

  • mon 11/19 - principal components analysis

  • wed 11/21 - non-negative matrix factorization

  • fri 11/23 - k-means

  • mon 11/26 - mixtures of gaussians

  • wed 11/28 - agglomerative clustering

  • fri 11/30 - tbd - HOMEWORK 7 DUE

  • mon 12/3 - tbd

  • wed 12/5 - guest lecture!

  • fri 12/7 - tbd

  • mon 12/10 - last day of class - another tbd!