Lectures
You can download the lectures here. We will try to upload lectures prior to their corresponding classes.
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(foml-01) Unsupervised Learning - 1
tl;dr: Overview of Unsupervised Learning with a focus on K-means clustering.
[slides]
Suggested Reading
- Chapter 14 of Introduction to Statistical Learning textbook by Gareth James et al.
- Chapter 22 of Understanding ML: From Theory to Algorithms book by Shai Shalev-Shwartz et al.
- Chapter 9 of PR and ML book by Christopher M Bishop
- Chapter 7 of Introduction to Machine Learning by Ethem Alpaydın
- Chapter 25 of Machine Learning: a Probabilistic Perspective by Kevin Murphy
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(foml-02) Unsupervised Learning - 2
tl;dr: Key Unsupervised Learning techniques: GMM, Hierarchical Clustering, and PCA overview.
[Hierarchical] [GMM] [PCA]
Suggested Reading
- Chapter 14 of Introduction to Statistical Learning textbook by Gareth James et al.
- Clustering with Gaussian Mixtures by Andrew W. Moore, CMU
- Chapter 23 and 24 of Understanding ML: From Theory to Algorithms book by Shai Shalev-Shwartz et al.
- Chapter 9 of PR and ML book by Christopher M Bishop
- Chapter 6 and 7 of Introduction to Machine Learning by Ethem Alpaydın
- Chapter 11 of Machine Learning: a Probabilistic Perspective by Kevin Murphy
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(foml-03) Neural Networks
tl;dr: Overview of Neural Networks, Regularization, Gradient Descent as a method of minimizing the loss function, and backpropagation.
[NN] [Gradient Descent] [Backprop-1] [Backprop-2]
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(foml-04) Tree based Methods
tl;dr: Tree based methods for Regression and Classification with examples, Decision trees, Bagging, Random Forest, and Boosting.
[Slides]