1. Introduction; 2. Binary regression: the logit model; 3. Generalized linear models; 4. Modeling of binary data; 5. Alternative binary regression models; 6. Regularization and variable selection for parametric models; 7. Regression analysis of count data; 8. Multinomial response models; 9. Ordinal response models; 10. Semi- and nonparametric generalized regression; 11. Tree-based methods; 12. The analysis of contingency tables: log-linear and graphical models; 13. Multivariate response models; 14. Random effects models; 15. Prediction and classification; Appendix A. Distributions; Appendix B. Some basic tools; Appendix C. Constrained estimation; Appendix D. Kullback-Leibler distance and information-based criteria of model fit; Appendix E. Numerical integration and tools for random effects modeling.