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Pre-requisite |
Ch 2 |
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| 08/29/2017 |
Overview of Syllabus, Intro to ML, Nearest neighbor classifiers |
Ch 1.1 - 1.42 |
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Aug29Lecture.pdf |
082917.1.pdf, 082917.2.pdf |
| 08/31/2017 |
Generalization, Cross-validation |
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Aug31Lecture.pdf |
083117.1.pdf, 083117.2.pdf |
| 09/05/2017 |
Bias/Variance Tradeoff, Bayes optimal |
Ch 6.4.4, 3.2, 3.5, |
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Sep05Lecture.pdf |
090517.1.pdf, 090517.2.pdf, 090517.3.pdf |
| 09/07/2017 |
Overfitting, Probabilistic Models (Naive Bayes), Maximum Likelihood (linear / logistic regression) |
Ch 3.5, 8.2 |
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Sep07Lecture.pdf, Sep07Addendum.pdf |
090717.1.pdf, 090717.2.pdf, 090717.3.pdf |
| 09/12/2017 |
Optimization: Convexity, Gradient Descent (Linear / Logistic) |
Ch 7.2, 7.3, 8.3.1 - 8.3.4 |
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Sep12Lecture.pdf |
091217.1.pdf, 091217.2.pdf |
| 09/14/2017 |
Penalized maximum likelihood |
Ch 7.5. 8.3.6, |
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Sep14Lecture.pdf |
091417.1.pdf, 091417.2.pdf, 091417.3.pdf |
| 09/19/2017 |
Variable Selection (Lasso) |
Ch 13.1, 13.2.3., 13.3 - 13.3.1, 13.4.1 |
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Sep19Lecture.pdf |
091917.1.pdf, 091917.2.pdf |
| 09/21/2017 |
Decison Trees |
Ch 16.2 |
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Sep21Lecture.pdf |
092117.1.pdf, 092117.2.pdf, 092117.3.pdf |
| 09/26/2017 |
Adaboost |
Ch 16.4 |
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Sep26Lecture.pdf |
092617.1.pdf, 092617.2.pdf, 092617.3.pdf |
| 09/28/2017 |
Bagging (Random Forests), Surrogate Losses |
Ch 16.2.5 |
Breiman, Leo. "Bagging predictors." Machine learning 24.2 (1996): 123-140. |
Sep28Lecture.pdf |
092817.1.pdf, 092817.2.pdf |
| 10/03/2017 |
Kernel methods |
Ch 14.1, 14.2, 14.4, |
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Oct03Lecture.pdf |
100317.1.pdf, 100317.2.pdf, 100317.3.pdf |
| 10/05/2017 |
Kernel Ridge Regression, SVM, |
Ch 14.43, 14.5, 14.7 |
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Oct05Lecture.pdf |
100517.1.pdf, 100517.2.pdf |
| 10/10/2017 |
Representer Theorem |
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Oct10Lecture.pdf |
101017.1.pdf, 101017.2.pdf |
| 10/12/2017 |
In Class EXAM #1 |
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| 10/17/2017 |
Optimization: Online Learning, Stochastic Gradient Descent, Perceptron |
Ch 8.5 - 8.54 |
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Oct17Lecture.pdf |
101717.1.pdf, 101717.2.pdf, 101717.3.pdf |
| 10/19/2017 |
Backpropagation |
Ch 16.5, 16.54 |
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Oct19Lecture.pdf |
101917.1.pdf, 101917.2.pdf |
| 10/24/2017 |
MLP |
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Oct24Lecture.pdf |
102417.1.pdf, 102417.2.pdf |
| 10/26/2017 |
MLP - Part II |
Ch 16.5.1 |
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Oct26Lecture.pdf |
102617.1.pdf, 102617.2.pdf |
| 10/31/2017 |
Convolutional NN's, Project discussion |
Ch 16.5.2 |
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Oct31Lecture.pdf |
103117.1.pdf, 103117.2.pdf |
| 11/02/2017 |
Convolutional NN's - 2, Recurrent Neural Networks |
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Nov2Lecture.pdf |
110217.1.pdf, 110217.2.pdf, 110217.3.pdf |
| 11/07/2017 |
Exam review |
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110717.1.pdf, 110717.2.pdf |
| 11/09/2017 |
In Class EXAM #2 |
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| 11/14/2017 |
LSTM |
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Nov14Lecture.pdf |
111417.1.pdf, 111417.2.pdf, 111417.3.pdf |
| 11/16/2017 |
Unsupervised Learning |
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Nov16Lecture.pdf |
111617.1.pdf, 111617.2.pdf, 111617.3.pdf |
| 11/21/2017 |
FALL BREAK |
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| 11/23/2017 |
FALL BREAK |
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| 11/28/2017 |
Expectation Maximization, Mixture of Gaussians |
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Nov28Lecture.pdf |
112817.1.pdf, 112817.2.pdf |
| 11/30/2017 |
Expectation Maximization, Mixture of Gaussians |
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Nov30Lecture.pdf |
113017.1.pdf, 113017.2.pdf |
| 12/05/2017 |
Probabilistic PCA |
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Dec05Lecture.pdf |
120517.1.pdf, 120517.2.pdf |
| 12/07/2017 |
Variational Autoencoders |
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Dec07Lecture.pdf |
120717.1.pdf |
| 12/12/2017 |
Generative Adverserial Networks |
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Dec12Lecture.pdf |
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FINAL PROJECT DUE, LITERATURE REVIEW DUE (FINALS DAY, DEC 19) |
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