Machine Learning
Maestría en Ingeniería - Ingeniería de Sistemas y Computación

Departamento de Ingeniería de Sistemas e Industrial
Universidad Nacional de Colombia
Ing. Fabio A. González O., Ph.D.
Of. 114, Edif. Nuevo de Ingeniería


Course Description


The main goal of Machine Learning (ML) is the development of systems that are able to autonomously change their behavior based on experience. ML offers some of the more effective techniques for knowledge discovery in large data sets. ML has played a fundamental role in areas such as bioinformatics, information retrieval, business intelligence and autonomous vehicle development.

The main goal of this course is to study the computational, mathematical and statistical foundations of ML, which are essential for the theoretical analysis of existing learning algorithms, the development of new algorithms and the well-founded application of ML to solve real-world problems.



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
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
Assignment 2

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
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 27-Nov 1)
Carlos M. Estevez-Edwin Ovalle [Bleakley07][Ben-Hur05]
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 8-Nov 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 Non-negative 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 complex-structured and non-structured 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

Dec 13