Syllabus

1. Regression Analysis (estimators, test theory, OLS)
2. Regression Diagnostics (Gauss-Markov theorem, GM assumptions, omitted variable bias, panel data analysis)
3. Logistic and Poisson Regression (GLMs, logit, probit, poisson regression)
4. Naïve Bayes and Bayes Nets (Bayes rule, learning Bayes nets, d-separation)
5. Decision Tree Classifiers (entropy, C4.5, CART, tree pruning)
6. Data Preparation and Causal Inference (practical data preparation, causal inference, IV, PSM, multiple imputation, etc.)
7. Model Selection (gain curves, lift, ROC, bias-variance tradeoff) and Introduction to the Analytics Cup (R tutorial)
8. Ensemble Methods and Clustering (bagging, random forests, boosting, hierarchical clustering, k-means, expectation maximization)
9. High-Dimensional Problems (PCA, SVD, PCA regression, PLS, ridge regression, LASSO)
10. Association Rules and Recommenders (APRIORI, collaborative filtering: SVD-based and nearest neighbor), Neural Networks Intro
11. Neural Networks (feed-forward networks, backpropagation, gradient descent)
12. Convex Optimization (gradient descent, Newton's method)
13. Presentation Analytics Cup

Literature

The presentation slides for the lectures and tutorials are accessible via Moodle. The contents of the lectures can be found in chapters from the following textbooks:

• Trevor Hastie, Jerome Friedman, Robert Tibshirani: Elements of Statistical Learning, Springer, 2016. (E-Book)
• Ian Witten, Eibe Frank, Mark Hall, Christopher Pal: Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kauffman, 2016 (E-Book)
• James H. Stock and Mark W. Watson: Introduction to Econometrics, Pearson Education.
• Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani: An Introduction to Statistical Learning, Springer, 2014 (E-Book)
• Hadley Wickham, Garrett Grolemund: R for Data Science, 2017 (E-Book)