Machine Learning

Course Description

Objective

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.

Methodology

Professor's lectures on fundamental topics

Practical assignments and exercises to be solved by students

Technical papers' review and presentation by students

Final project

Written and practical tests. Students must show a good grasp of concepts and skills covered in the course.

Contents

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 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
Project:			Dec 13

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 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

Project:

Dec 13

Grading

Assignments 40%

Exams 30%

Presentation 15%

Final project 15%

References

[Alp10] Alpaydin, E. 2010 Introduction to Machine Learning, 2Ed. The MIT Press.

[Bis06] Bishop, C. 2006 Pattern Recognition and Machine Learning. Springer-Verlag.

[Mit97] Mitchell, T. M. 1997 Machine Learning. 1st. McGraw-Hill Higher Education.

[DHS00] Duda, R. O., Hart, P. E., and Stork, D. G. 2000 Pattern Classification (2nd Edition). Wiley-Interscience.

[HTF01] Hastie, T. and Tibshirani, R. and Friedman. 2001 The elements of statistical learning: data mining, inference, and prediction. Springer.

[SC04] Shawe-Taylor, J. and Cristianini, N. 2004 Kernel Methods for Pattern Analysis. Cambridge University Press.

[TSK05] Pang-Ning Tan, Michael Steinbach, Vipin Kumar, 2005, Introduction to Data Mining, Addison-Wesley.

[CST00] Cristianini, N. and Shawe-Taylor, J., 2000, An introduction to support Vector Machines: and other kernel-based learning methods,, Cambridge Univ Press.

[SS02] Scholkopf, B. and Smola, A.J., 2002, Learning with kernels, MIT Press.

[Bak07] Bakir, G. (Ed), 2007, Predicting Structured Data, MIT Press.

[OCW-ML] 6.867 Machine Learning, Fall 2006, MIT OpenCourseWare.

[STANFD-ML] Andrew Ng, CS229 Machine Learning, Stanford University

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Machine Learning
Maestría en Ingeniería - Ingeniería de Sistemas y Computación

Contents

Course Description

Objective

Methodology

Contents

Grading

References

Assignments

Resources

Machine Learning Maestría en Ingeniería - Ingeniería de Sistemas y Computación

Contents

Course Description

Objective

Methodology

Contents

Grading

References

Assignments

Resources

Machine Learning
Maestría en Ingeniería - Ingeniería de Sistemas y Computación