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
[Alp04] Cap 1,2
[DHS00] A.1, A.2
Assignment 1
Assignment 2
The great robot race

Introduction to Machine Learning (notes)
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
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

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)

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 complex-structured and non-structured data

Paper presentation:
Rodolfo Torres
Paulo Guillén
John Leithon
Final Exam 



Additional references