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• Note 2- Linear Regression
• Note 3- Feature Engineering
• Note 4- MLE and MAP for Regression
• Note 5- Bias-Variance Tradeoff
• Note 6- Multivariate Gaussians
• Note 7- MLE and MAP Part 2
• Note 8- Kernels and Ridge Regression
• Note 9- Total Least Squares
• Note 10- PCA
• Note 11- CCA
• Note 12- Optimization
• Note 13- Gradient Descent
• Note 14- Neural Networks
• Note 15- Training Neural Networks
• Note 16- Generative vs. Discriminative Classification
• Note 18- Gaussian Discriminant Analysis
• Note 19- Clustering
• Note 20- Support Vector Machines
• Note 21- Generalization and Stability
• Note 22- Duality
• Note 25- Decision Trees
• Note 26- Boosting
• Note 27- Convolutional Neural Networks
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