Machine Learning

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

Profesorado

Responsable

• Marta Arias Vicente ( )

Otros

• Carlos Escolano Peinado ( )
• Ignasi Gómez Sebastià ( )

Horas semanales

Competencias

Transversal Competences

Information literacy

• CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Third language

• CT5 - Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.

Entrepreneurship and innovation

• CT1 - Know and understand the organization of a company and the sciences that govern its activity; have the ability to understand labor standards and the relationships between planning, industrial and commercial strategies, quality and profit. Being aware of and understanding the mechanisms on which scientific research is based, as well as the mechanisms and instruments for transferring results among socio-economic agents involved in research, development and innovation processes.

Basic

• CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
• CB7 - Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
• CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
• CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.
• CB10 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.

Generic Technical Competences

Generic

• CG2 - Identify and apply methods of data analysis, knowledge extraction and visualization for data collected in disparate formats

Technical Competences

Especifics

• CE6 - Design the Data Science process and apply scientific methodologies to obtain conclusions about populations and make decisions accordingly, from both structured and unstructured data and potentially stored in heterogeneous formats.
• CE7 - Identify the limitations imposed by data quality in a data science problem and apply techniques to smooth their impact
• CE10 - Identify machine learning and statistical modeling methods to use and apply them rigorously in order to solve a specific data science problem
• CE12 - Apply data science in multidisciplinary projects to solve problems in new or poorly explored domains from a data science perspective that are economically viable, socially acceptable, and in accordance with current legislation
• CE13 - Identify the main threats related to ethics and data privacy in a data science project (both in terms of data management and analysis) and develop and implement appropriate measures to mitigate these threats

Objetivos

1. Formulate the problem of (machine) learning from data, and know the different machine learning tasks, goals and tools.
   Competencias relacionadas: CB6, CB7, CB8, CB10,
2. 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: CT4, CT5, CT1, CG2, CE6, CE7, CE10, CE12, CE13, CB6, CB7, CB8, CB9, CB10,
3. To be able to solve concrete machine learning problems with available open-source software
   Competencias relacionadas: CT4, CT5, CG2, CE6, CE7, CE10, CE12, CE13, CB6, CB9,

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.
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: 9780471681823
  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, 2020. ISBN: 9780262043793
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004193529706711&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=alma91003533949706711&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: 9780387952840
  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: 9780471056690
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991002131619706711&context=L&vid=34CSUC_UPC:VU1&lang=ca

Web links

• Official website of a popular library for machine learning in python which we will use extensively https://scikit-learn.org/stable/

Capacidades previas