20010II 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 
Videos: The great robot race Wining the Darpa Grand Challenge Introduction to Machine Learning 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 2 (dataset) Assignment 3 
Videos: Embracing uncertainty: the new machine intelligence Presentations: Fabián Giraldo [Bishop07] Juan Gabriel Bobadilla [Bishop08] 
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 
Presentations: Angélica Veloza [Quadrianto10] Juan Guillermo Carvajal [Chen09] 

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 
Presentations: Carlos Arias [Finley05] Alfredo Espitia [Joachims09] Rubén Manrique [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) 
Presentations: Angel Cruz [Dietterich98] Arles Rodríguez [Domingos99] 

6. Combining multiple classifiers 6.1 Voting 6.2 Error correcting codes 6.3 Bagging 6.4 Boosting 
[Alp04] Cap 15  Presentations: Juan Carlos León [Viola04] Carlos Sierra [Breiman01] 

7. Learning on complexstructured and nonstructured data 7.1 Sructured output prediction 7.2 Markov Random Fields 7.3 Structured SVM 
Presentations: Javier Sandoval Leandro Liu [Cabestany05] 

Final Exam: Nov 23rd  
Project:  Proposal: Nov 9th  Final: Nov 30th 