The main goal of Machine
(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.
- Professor's lectures on fundamental topics
- Practical assignments and exercises to be solved
- 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.
|Brief Introduction to ML
[Mit97] Cap 1
[Alp10] Cap 1,2
[DHS00] A.1, A.2
Machine Learning: A Love Story
Winning The DARPA Grand Challenge
Algebra and Probability Review (part 1 Linear Algebra,
part 2 Probability)
|2. Bayesian decision theory
2.1 A review of probability theory
2.3 Loss and risk
2.6 Maximum likelihood estimation
2.7 Bayesian estimation
2.8 Parametric Classification
[DHS00] Chap 3
Bias and Variance notebook
|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
uncertainty: the new machine intelligence
|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
|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
introduction to ML, Smola
Vector Machine Tutorial, Weston
Introduction to kernel methods (part 2)
[Lin02] Marco Calvo, Roger Guzmán
[Joachims09] Juan C. Espinoza,
|5. Performance evaluation
5.1 Performance evaluation in supervised learning
5.2 Performance evaluation in unsupervised learning
5.3 Hypothesis testing
|[Alp10] Chap 19
8 (Sect. 8.5)
[Fawcett06] Oscar Paruma, William Duarte
[Demsar06] Miguel Ballesteros, Felipe Montaña
|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
Semantic Indexing, Prasad
Learning for BOF, Lazebnik
NMF for Multimodal Image Retrieval,
[Ding08] Santiago Baldrich, Octavio Márquez
[Dhillon04] Viviana Beltrán, Esteban Páez
|7.Deep Learning|| || ||Presentations:|
[Salakhutdinov09] Alejandro Gallego, Javier Guaje
[Shin13] Germán Sosa, Víctor Forero
|8.Large Scale Machine Learning|| || Assignment 4||Presentations:|
[Weston10] Daniel Grass, Daniel Santiago
[Le12] Diego Rojas, Andrés Peñuela
- Assignments 40%
- Exams 30%
- Presentation 15%
- Final project 15%
- [Alp10] Alpaydin, E. 2010 Introduction to Machine Learning, 2Ed.
The MIT Press.
- [Mur12] Murphy, Kevin P. Machine learning: a probabilistic perspective. The MIT Press, 2012.
- [Bis06] Bishop, C. 2006 Pattern Recognition and Machine Learning.
- [Mit97] Mitchell, T. M. 1997 Machine Learning. 1st.
- [DHS00] Duda, R. O., Hart, P. E., and Stork, D. G. 2000
Classification (2nd Edition). Wiley-Interscience.
- [HTF01] Hastie, T. and Tibshirani, R. and Friedman. 2001
statistical learning: data mining, inference, and prediction. Springer.
- [SC04] Shawe-Taylor, J. and Cristianini, N. 2004 Kernel
for Pattern Analysis. Cambridge University Press.
- [TSK05] Pang-Ning Tan, Michael Steinbach, Vipin Kumar,
2005, Introduction to Data Mining, Addison-Wesley.
Cristianini, N. and Shawe-Taylor, J., 2000, An introduction to support
Vector Machines: and other kernel-based learning methods,, Cambridge
- [SS02] Scholkopf, B. and Smola, A.J., 2002, Learning with
kernels, MIT Press.
- [Bak07] Bakir, G. (Ed), 2007, Predicting Structured Data, MIT
- [OCW-ML] 6.867
Machine Learning, Fall 2006, MIT OpenCourseWare.
- [STANFD-ML] Andrew Ng, CS229 Machine
Learning, Stanford University
- SciPy: scientific, mathematical, and engineering package for Python
- scikit-learn: machine learning Scipy add-on
- Kaggle: datascience competition, many interesting data sets and different competitions with prizes.