Topics covered:
1. a. Supervised vs. unsupervised learning b. Bias variance trade-off
2. a. Decision theory: cost, loss, risk, objective
b. Maximum likelihood estimation, frequentist vs Bayesian
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3. a. Linear algebra review b. Principal component analysis
c. Basic models: k-means clustering, k-NN regression, k-NN classification
4. a. Regression: linear regression
5. b. Optimization: convex, gradient descent, Newton¡¯s method
5. a. Cross validation, Model selection, Bootstrap, 1-SD rule b. Regularization, shrinkage 6. a. Classification: naive Bayes b. Classification: logistic regression
c. Classification: linear & quadratic discriminant analysis
7. a. Classification: ROC curves b. Classification: support vector machines
8. a. Classification: support vector machines with kernels
b. Optimization: convexity and constraints
9. a. Regression: non-linear regression
b. Decision trees, bagging, random forests, Boosting
10. a. Neural networks
b. Cluster analysis: hierarchical, EM-algorithm, Gaussian mixture, model order selection 11. a. Information theory b. Matrix completion
12. a. Hidden Markov models
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