Aprendizaje Automático

• Horas semanales
• Competencias
• Objetivos
• Contenidos
• Actividades
• Metodología docente
• Método de evaluación
• Bibliografía
• Web links
• Capacidades previas

Profesorado

Responsable

• Mario Martín Muñoz ( )
• Marta Arias Vicente ( )

Horas semanales

Competencias

Competencias Técnicas Genéricas

Genéricas

• CG1 - Capacidad para aplicar el método científico en el estudio y análisis de fenómenos y sistemas en cualquier ámbito de la Informática, así como en la concepción, diseño e implantación de soluciones informáticas innovadoras y originales.
• CG3 - Capacidad para el modelado matemático, cálculo y diseño experimental en centros tecnológicos y de ingeniería de empresa, particularmente en tareas de investigación e innovación en todos los ámbitos de la Informática.
• CG5 - Capacidad para aplicar soluciones innovadoras y realizar avances en el conocimiento que exploten los nuevos paradigmas de la Informática, particularmente en entornos distribuidos.

Competencias Transversales

Razonamiento

• CTR6 - Capacidad de razonamiento crítico, lógico y matemático. Capacidad para resolver problemas dentro de su área de estudio. Capacidad de abstracción: capacidad de crear y utilizar modelos que reflejen situaciones reales. Capacidad de diseñar y realizar experimentos sencillos, y analizar e interpretar sus resultados. Capacidad de análisis, síntesis y evaluación.

Básicas

• CB6 - Que los estudiantes sepan aplicar los conocimientos adquiridos y su capacidad de resolución de problemas en entornos nuevos o poco conocidos dentro de contextos más amplios (o multidisciplinares) relacionados con su área de estudio.

Competencias Técnicas de cada especialidad

Específicas comunes

• CEC1 - Capacidad para aplicar el método científico en el estudio y análisis de fenómenos y sistemas en cualquier ámbito de la Informática, así como en la concepción, diseño e implantación de soluciones informáticas innovadoras y originales.
• CEC2 - Capacidad para el modelado matemático, cálculo y diseño experimental en centros tecnológicos y de ingeniería de empresa, particularmente en tareas de investigación e innovación en todos los ámbitos de la Informática.

Objetivos

1. Formulate the problem of (machine) learning from data, and know the different machine learning tasks, goals and tools.
   Competencias relacionadas: CG3, CEC1,
2. Organize the workflow for solving a machine learning problem, analyzing the possible options and choosing the most appropriate to the problem at hand
   Competencias relacionadas: CB6, CEC1, CEC2, CTR6, CG5,
3. Ability to decide, defend and criticize a solution to a machine learning problem, arguing the strengths and weaknesses of the approach. Additionally, ability to compare, judge and interpret a set of results after making a hypothesis about a machine learning problem
   Competencias relacionadas: CG1, CEC1, CEC2, CTR6,
4. Understand and know how to apply least squares techniques for solving supervised learning problems
   Competencias relacionadas: CG3, CEC2, CTR6,
5. Understand and know how to apply techniques for single and multilayer neural networks for solving supervised learning problems
   Competencias relacionadas: CG3, CB6, CEC2, CTR6,
6. Understand and know how to apply support vector machines for solving supervised learning problems
   Competencias relacionadas: CG3, CB6, CEC2, CTR6, CG5,
7. Understand and formulate different theoretical tools for the analysis, study and description of machine learning systems
   Competencias relacionadas: CG3, CTR6, CG5,
8. Understand and know how to apply the basic techniques for solving unsupervised learning problems
   Competencias relacionadas: CG3, CB6,

Contenidos

1. Introduction to Machine Learning
   General information and basic concepts. Overview to the problems tackled by machine learning techniques. Supervised learning (classification and regression), unsupervised learning (clustering and density estimation) and semi-supervised learning (reinforcement and transductive). Examples.
   
   General information and basic concepts. Overview to the problems tackled by machine learning techniques. Supervised learning (classification and regression), unsupervised learning (clustering and density estimation) and semi-supervised learning (reinforcement and transductive). Examples.
   
   General information and basic concepts. Overview to the problems tackled by machine learning techniques. Supervised learning (classification and regression), unsupervised learning (clustering and density estimation) and semi-supervised learning (reinforcement and transductive). Examples.
2. Supervised machine learning theory
   The supervised Machine Learning problem setup. Classification and regression problems. Bias-variance tradeoff. Regularization. Overfitting and underfitting. Model selection and resampling methods.
3. Linear methods for regression
   Error functions for regression. Least squares: analytical and iterative methods. Regularized least squares. The Delta rule. Examples.
4. Linear methods for classification
   Error functions for classification. The perceptron algorithm. Novikoff's theorem. Separations with maximum margin. Generative learning algorithms and Gaussian discriminant analysis. Naive Bayes. Logistic regression. Multinomial regression.
5. Artificial neural networks
   Artificial neural networks: multilayer perceptron and a peak into deep learning. Application to classification and to regression problems.
6. Kernel functions and support vector machines
   Definition and properties of Kernel functions. Support vector machines for classification and regression problems.
7. Unsupervised machine learning
   Unsupervised machine learning techniques. Clustering algorithms: EM algorithm and k-means algorithm.
8. Ensemble methods
   Bagging and boosting methods, with an emphasis on Random Forests

Actividades

Actividad Acto evaluativo

Metodología docente

Método de evaluación

Bibliografía

Básica:

• Pattern recognition and machine learning - Bishop, C.M, Springer, 2006. ISBN: 0387310738
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003157379706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Learning from data: concepts, theory, and methods - Cherkassky, V.S.; Mulier, F, John Wiley, 2007. ISBN: 0471681822
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003624509706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Introduction to machine learning - Alpaydin, E, The MIT Press, 2014. ISBN: 9780262028189
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004070619706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Machine learning: a probabilistic perspective - Murphy, K.P, MIT Press, 2012. ISBN: 9780262018029
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003972109706711&context=L&vid=34CSUC_UPC:VU1&lang=ca

Complementaria:

• Neural networks and learning machines - Haykin, S.S, Prentice Hall , 2009. ISBN: 9780131471399
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003533949706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• The elements of statistical learning: data mining, inference, and prediction - Hastie, T.; Tibshirani, R.; Friedman, J, Springer , 2009. ISBN: 9780387848570
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003549679706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Pattern classification - Duda, R.O.; Hart, P.E.; Stork, D.G, John Wiley & Sons , 2001. ISBN: 0471056693
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991002131619706711&context=L&vid=34CSUC_UPC:VU1&lang=ca

Web links

• Official website of the R programming language http://cran.r-project.org/
• Repository of educational videos (in English) on the topics of the course. http://videolectures.net/Top/Computer_Science/Machine_Learning/
• Course on Machine Learning at Stanford University, by Andrew Ng http://www.academicearth.org/courses/machine-learning

Capacidades previas