2009I Departamento de Ingeniería de Sistemas e Industrial Universidad Nacional de Colombia 
Ing. Fabio A. González O., Ph.D. Of. 114, Edif. Nuevo de Ingeniería fagonzalezo_at_unal.edu.co 
Topic  Material  Assignments  Presentations 

1. Introduction 
Brief
Introduction to ML [Mit97] Cap 1 [Alp04] Cap 1,2 [DHS00] A.1, A.2 
Assignment
1 Assignment 2 
Videos: The great robot race Introduction to Machine Learning (notes) Review: Linear Algebra and Probability Review (part 1 Linear Algebra, part 2 Probability) 
2. Bayesian decision theory 2.1 A review of probability theory 2.2 Classification 2.3 Lost and risk 2.4 Naive Bayes classifier 2.5 Bayesian Networks 2.6 Maximum likelihood estimation 2.7 Bayesian estimation 2.8 Parametric Classification 2.9 Expectation Maximization 
[Alp04] Chap
3,
Chap
4, Chap 7 (Sect. 7.4) [DHS00] Chap 3 [Tenenbaum06] 
Assignment 3  Video: Generative Models for Visual Objects and Object Recognition via Bayesian Inference Paper presentation: Jose Luis Morales [Rabiner89] Luis Ochoa [Myers99] Omar Erazo [Yesidia03] 
3. Kernel methods 3.1 The kernel trick 3.2 Kernel ridge regression 3.3 Kernel functions 3.4 Other kernel Algorithms 3.5 Kernels in complex structured data 
[SC04] Chap 2 Introd. to kernel methods 
Video: Weighted Transducers and Rational Kernels Paper presentation: Andrés Castillo [Borgwardt05] 

4. Support vector learning 4.1 Support vector machines 4.2 Regularization and model complexity 4.3 Risk and empirical risk 4.4 SVM variations 
[Alp04] Chap
4 (Sect. 4.3, 4.7, 4.8), Chap 10 (Sect. 10.9) An introduction to ML, Smola Support Vector Machine Tutorial, Weston 
Assignment 4  Video: Large Scale Learning with String Kernels Paper presentation: Anyela Chavarro [Tong01] Wilfredy Santamaría [Hsu02] Andrés Ramirez [Smola04] 
5. Performance evaluation 5.1 Performance evaluation in supervised learning 5.2 Performance evaluation in unsupervised learning 5.3 Hypothesis testing 
[Alp04] Cap 14 [TSK05] Chap 8 (Sect. 8.5) 
Video: Paper presentation: Jaime Beltrán John Moreno Álvaro Uzaheta 

6. Combining multiple classifiers 6.1 Voting 6.2 Error correcting codes 6.3 Bagging 6.4 Boosting 
[Alp04] Cap 15  Paper presentation: Raul Torres Andrés Jaque Eduardo Ortega 

7. Learning on complexstructured and nonstructured data  Paper presentation: Rodolfo Torres Paulo Guillén John Leithon 

Final Exam  
Project 