Machine Learning I

• Teachers
• Weekly hours
• Competences
• Objectives
• Contents
• Activities
• Teaching methodology
• Evaluation methodology
• Bibliography
• Previous capacities

Teachers

Person in charge

• Marta Arias Vicente ( )

Others

• Alexis Molina Martinez de los Reyes ( )
• Luis Antonio Belanche Muñoz ( )

Weekly hours

Competences

Technical Competences

Technical competencies

• CE1 - Skillfully use mathematical concepts and methods that underlie the problems of science and data engineering.
• CE3 - Analyze complex phenomena through probability and statistics, and propose models of these types in specific situations. Formulate and solve mathematical optimization problems.
• CE8 - Ability to choose and employ techniques of statistical modeling and data analysis, evaluating the quality of the models, validating and interpreting them.
• CE9 - Ability to choose and employ a variety of automatic learning techniques and build systems that use them for decision making, even autonomously.

Transversal Competences

Transversals

• CT3 - Efficient oral and written communication. Communicate in an oral and written way with other people about the results of learning, thinking and decision making; Participate in debates on topics of the specialty itself.
• CT4 [Avaluable] - Teamwork. Be able to work as a member of an interdisciplinary team, either as a member or conducting management tasks, with the aim of contributing to develop projects with pragmatism and a sense of responsibility, taking commitments taking into account available resources.
• CT7 - Third language. Know a third language, preferably English, with an adequate oral and written level and in line with the needs of graduates.

Generic Technical Competences

Generic

• CG1 - To design computer systems that integrate data of provenances and very diverse forms, create with them mathematical models, reason on these models and act accordingly, learning from experience.
• CG2 - Choose and apply the most appropriate methods and techniques to a problem defined by data that represents a challenge for its volume, speed, variety or heterogeneity, including computer, mathematical, statistical and signal processing methods.

Objectives

1. Formulate the problem of automatic learning from data, and get to know the types of tasks that can be given.
   Related competences: CE1, CE9, CG1, CG2,
2. Organize the resolution flow of a machine learning problem, analyzing the possible options and choosing the most suitable for the problem.
   Related competences: CE1, CE9, CT4, CT7, CG1, CG2,
3. Decide, defend and criticize a solution to a machine learning problem, arguing the strong and weak points of the approach.
   Related competences: CE9, CT3, CT4, CG2,
4. Know and know how to apply linear techniques to solve supervised learning problems.
   Related competences: CE3, CE8, CG2,
5. Know and know how to apply mono and multilayer neural network techniques to solve supervised learning problems.
   Related competences: CE8, CE9, CG2,
6. Know and know how to apply support vector machines to the resolution of supervised learning problems.
   Related competences: CE8, CE9, CG2,
7. Know and know how to apply the basic techniques for the resolution of unsupervised learning problems, with emphasis on data clustering tools.
   Related competences: CE8, CE9, CG2,
8. Know and know how to apply the basic techniques for solving reinforcement learning problems.
   Related competences: CE8, CE9, CG2,
9. Know and know how to apply ensemble techniques to solve supervised learning problems.
   Related competences: CE8, CE9, CG2,

Contents

1. Introduction to Machine Learning
   General information and basic concepts. Description and approach of problems attacked by automatic learning. Supervised learning (regression and classification), non-supervised (clustering) and semi-supervised (reinforcement and transductive). Modern examples of application.
2. Unsupervised machine learning: clustering
   Definition and approach of unsupervised machine learning. Introduction to clustering. Probabilistic algorithms: k-means and Expectation-Maximization (E-M).
3. Supervised machine learning (I): linear regression methods
   Maximum likelihood for regression. Errors for regression. Least squares: analytical (pseudo-inverse and SVD) and iterative ( gradient descent) methods. Notion of regularization. L1 and L2 regularized regression: algorithms ridge regression, LASSO and Elastic Net.
4. Supervised machine learning (II): linear methods for classification
   Maximum likelihood for classification. Error functions for classification. Bayesian Generative Classifiers: LDA/QDA/RDA, Naïve Bayes and k-nearest neighbours.
5. Hierarchical methods: decision trees
   General construction of decision trees. Split criteria: gain in entropy and Gini. Regularization in decision trees. CART trees for regression and classification.
6. Ensemble methods
   Introduction to ensemble methods. Bagging and Random Forests. Boosting. Adaboost and variants.
7. Kernel based learning methods
   Introduction to learning with kernel functions. Regularized kernelized linear regression. Basic kernel functions. Complexity and generalization: Vapnik-Chervonenkis dimension. Support Vector Machine.

Activities

Activity Evaluation act

Teaching methodology

Evaluation methodology

Bibliography

Basic:

• 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
• 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
• 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
• The elements of statistical learning: data mining, inference, and prediction - Hastie, T.; Tibshirani, R.; Friedman, J, Springer, 2009. ISBN: 0387848576
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003549679706711&context=L&vid=34CSUC_UPC:VU1&lang=ca

Previous capacities