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Jan 13 |
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Prolegomena. Course introduction
Slides |
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Jan 15 |
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Basic ML terminology with example
Slides |
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Jan 17 |
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Lab: Python libraries |
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Jan 22 |
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The nature of data. From object to vectors
Slides |
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Jan 24 |
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K nearest neighbors
Slides |
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Jan 27 |
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Linear regression. Simple linear regression and ordinary least squares
Slides |
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Jan 29 |
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Lab: Linear (and related) regression techniques |
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Jan 31 |
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Deriving linear regression
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Feb 3 |
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Newton's method and gradient descent |
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Feb 5 |
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Training regression using gradient descent
Slides |
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Feb 7 |
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Logistic regression. From linear regression to classification
Slides |
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Feb 10 |
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Lab: Applying logistic regression |
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Feb 12 |
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Training logistic regression
Slides |
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Feb 14 |
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Gaussian mixture models Probability and distributions
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Feb 19 |
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Lab: From histograms to Gaussians |
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Feb 24 |
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Expectation-maximization |
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Feb 26 |
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Support vector machines. Linear programming
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Mar 3 |
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Lab: Support vector classification |
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Mar 24 |
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Principal component analysis. PCA concepts
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Mar 26 |
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Lab: PCAs and facial recognition |
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Mar 28 |
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The math of PCA; eigenvectors and eigenvalues
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Apr 2 |
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Neural nets. The perceptron model, multilayer perceptrons
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Apr 7 |
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Perceptron training
Slides |
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Apr 9 |
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The feed-forward and back-propogation algorithms
Slides |
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Apr 11 |
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Deep learning: CNNs
Slides |
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Apr 14 |
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Deep learning: CNNs
Slides |
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Apr 16 |
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Lab: Deep learning |
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