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Week 1. Course introduction. Simple linear regression.
Week 2. Multiple linear regression.
Week 3. Multiple linear regression continu’ed
Week 4. Regression as a supervised learning problem.
Week 5. Regularization methods for regression problems. Ridge regression and lasso.
Week 6. Cross-validation. Unsupervised pre-processing. Grid search and hyper-parameter tuning.
Week 7. Remaining topics for regression problems.
Week 8. Introduction to classification. Logistic regression.
Week 9. Linear discriminant analysis. Quadratic discriminant analysis.
Week 10. Naive Bayes. K-nearest neighbors.
Week 11. Tree based methods. Bagging, Random forests, and Boosting.
Week 12. Unsupervised learning. Principal component analysis. Factor analysis.
Week 13. Clustering methods.
Week 14. Final review and examples. |