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 [Alp04] Cap 1,2 [DHS00] A.1, A.2	Assignment 1 Assignment 2	Videos: The great robot race Introduction to Machine Learning (notes) 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 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		Video: 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)	Assignment 5	Video: Paper presentation: Jaime Beltrán [Domingos99] Álvaro Uzaheta [Demsar06]
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 [Oza05] Andrés Jaque [Dietterich00] Eduardo Ortega [Mason00]
7. Learning on complex-structured and non-structured data			Paper presentation: Paulo Guillén John Leithon
Final Exam (June 16th)
Project (June 18th)

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

Videos:
The great robot race
Introduction to Machine Learning (notes)
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 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

Video:
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)

Assignment 5

Video:

Paper presentation:
Jaime Beltrán [Domingos99]
Álvaro Uzaheta [Demsar06]

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 [Oza05]
Andrés Jaque [Dietterich00]
Eduardo Ortega [Mason00]

7. Learning on complex-structured and non-structured data

Paper presentation:
Paulo Guillén
John Leithon

Final Exam (June 16th)

Project (June 18th)

Grading

Assignments 40%

Exams 30%

Presentation 15%

Final project 15%

References

[Alp04] Alpaydin, E. 2004 Introduction to Machine Learning (Adaptive Computation and Machine Learning). The MIT Press.

[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.

[OCW-ML] 6.867 Machine Learning, Fall 2002, MIT OpenCourseWare.

[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.

Additional references

MacKay, "Information Theory, Inference, and Learning Algorithms", 2003. (on-line version)

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Machine Learning
Maestría en Ingeniería - Ingeniería de Sistemas y Computación

Contents

Course Description

Objective

Methodology

Contents

Grading

References

Additional references

Assignments

Resources

Machine Learning Maestría en Ingeniería - Ingeniería de Sistemas y Computación

Contents

Course Description

Objective

Methodology

Contents

Grading

References

Additional references

Assignments

Resources

Machine Learning
Maestría en Ingeniería - Ingeniería de Sistemas y Computación