20011II 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 [Alp10] Cap 1,2 [DHS00] A.1, A.2 
Assignment 1  Videos: Machine Learning: A Love Story Rethinking the Automobile 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 Loss and risk 2.6 Maximum likelihood estimation 2.7 Bayesian estimation 2.8 Parametric Classification 
[Alp10] Chap
3,
Chap
4, Chap 5 [DHS00] Chap 3 [Tenenbaum06] 
Assignment 2 (dataset) 

3. Graphical models 3.1 Conditional independence 3.2 Naive Bayes classifier 3.3 Hidden Markov 2.5 Bayesian Networks 2.6 Belief propagation 2.7 Markov Random Fields 
[Alp10] Chap 16 Markov Random Fields 
Assignment 3  Video: Embracing uncertainty: the new machine intelligence Presentations: (Sept 8) Diana García  Alexander Urieles [Bishop07] Andrés Torres  Jorge Santos [Pardo05] 
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 [Alp10] Chap 13 Introd. to kernel methods 
Presentations: (Sept 20) Anibal Montero  Jorge Vanegas [Lazebnik06] Sergio Aristizabal  Iván Martínez [Tikk10] 

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 
[Alp10] Chap 13 An introduction to ML, Smola Support Vector Machine Tutorial, Weston 
Assignment 4  Presentations: (Sept 27Nov 1) Carlos M. EstevezEdwin Ovalle [Bleakley07][BenHur05] Felipe Cadena  Andrés Eslava [Smola04] 
5. Performance evaluation 5.1 Performance evaluation in supervised learning 5.2 Performance evaluation in unsupervised learning 5.3 Hypothesis testing 
[Alp10] Chap 19 [TSK05] Chap 8 (Sect. 8.5) 
Presentations: (Nov 8Nov 10) Ernesto Varela  Marla Barrera [Demsar06] Fabián Narvaez [Fawcett06] 

6. Unsupervised learning 6.1 Mixture densities 6.2 Expectation maximization 6.3 Mixture of latent variables models 6.4 Latent semantic analysis 6.5 Nonnegative matrix factorization 
[Alp10] Chap 7 Latent Semantic Indexing, Prasad Generative Learning for BOF, Lazebnik NMF for Multimodal Image Retrieval, González 
Presentations: (Nov 24) Santiago Pérez  David Bermeo [Ding08] John Arévalo  Fabio Parra [Dhillon04] 

7. Learning on complexstructured and nonstructured data 7.1 Sructured output prediction 7.2 Structured SVM 
Presentations: (Nov 29) Sebastián Otálora  Juan Gabriel Romero [Cabestany05] Sergio Ortiz  Alfredo Bayuelo [Joachims09] 

Final Exam:  Dec 1  
Project:  Dec 13 