Introduction to machine learning. |
Logistics, Slides, Notes, Scribbles | Bishop section 1.1 | Computing Machinery and Intelligence Probability Review-1 Probability Review-2 Linear Algebra Review |
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Introduction to machine learning. |
Notes, Scribbles | Bishop section 1.1, HTF section 2.3 |
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Statistical Decision Theory. |
Notes, Scribbles | HTF section 2.4, 2.5 | Singular Value Decomposition. Tutorial-1: Intro to Python. 14 Sep 6pm-8pm at ENGMC 304. |
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Linear Regression, Gradient Descent. |
Notes, Scribbles | Bishop section 3.1 | |||
Bias-variance tradeoff, Regularization. |
Notes, Scribbles | Bishop section 1.5.5, 3.1.4, 3.2 HTF section 3.4.1, 3.4.2 |
Linear Algebra review (7.1) in this pdf Lagrange Multiplier (Appendix E in Bishop) |
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Linear Classification. |
Notes, Scribbles | Bishop section 1.5.4, 4.1.1, 4.1.2 | |||
Indicator Regression, PCA, LDA. |
Notes, Scribbles | Bishop section 4.1.3, 4.1.4 this pdf for pca. |
Intro to convex optimization (page 91 to 102 in this book) | ||
Election Day. |
Tutorial-2: Intro to scikit-learn. 03 Oct 6pm-8pm at ENGMC 204. | ||||
GLMs, GDA, Evaluation metrics for classification. |
Notes, Scribbles | Bishop section 4.2 | No new reading. Finish previous reading. | ||
Thanksgiving. |
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Naive Bayes, Logistic Regression |
Notes, Scribbles | Bishop section 4.2.3, 4.3, 3.1.1. | Generative and discriminative classifiers by Tom Mitchell. | ||
Logistic Regression, Newton-Raphson Method. |
Notes, Scribbles | Bishop section 4.3.3, 1.5.4. | |||
Perceptron. |
Notes, no scribble. | Bishop section 4.1.7 HTF section 4.5 |
Bishop section 2.3 Tutorial-2: Intro to scikit-learn. 22 Oct 6pm-8pm at ENGMC 204. |
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Separating hyperplane, SVM. |
Notes | Bishop section 7.1 | |||
SVM, Non parametric Methods. |
Notes | Bishop section 7.1.2 | A Few Useful Things to Know about Machine Learning by Pedro Domingos | ||
Decision Trees. |
Notes | TSKK section 3.3 Mitchell Chapter 3 |
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Ensemble Learning: Bagging, Boosting. |
Notes | No new reading. Finish previous reading. | |||
Stacking, Neural Networks. |
Notes | ||||
Backpropagation Algorithm |
Notes | Efficient Backprop by LeCun et al. | |||
No Class. |
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Training Deep Neural Nets, Optimization. |
Notes | Optimization-1, Optimization-2 LSTMs, ConvNets |
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Convnets, RNNs, MLE, MAP, Bayesian Learning, Bayesian Linear Regression. |
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Kernel Methods, Gaussian Processes. |
No new reading. Focus on your project. | ||||
Clustering, K-means, DBScan. |
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Gaussian Mixture Models, EM Algorithm. |
No new reading. Focus on your project. | ||||
Course Summary and Practical Tips. |
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Frontiers in ML, What Next? |
No new reading. Focus on your project. |