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Machine Learning and Data Mining

Code 11491
Year 1
Semester S2
ECTS Credits 6
Workload PL(30H)/T(30H)
Scientific area Informatics
Learning outcomes At the end of the curricular unit the student should be able to understand and explain the main methods and algorithms used to automatic knowledge extraction from data and should be able of implementing simple processes to do this kind of tasks.
Syllabus Introduction - Machine learning and Data Mining – What they are and what are they useful for
Concept learning and general-to-specific ordering
Hypotheses evaluation
Main algorithms used in Machine Learning
Data Mining – Application of Machine Learning to real problems and data
Model for a standard Data Mining process
Main problems in Data Mining processes, and their solutions
Main Bibliography Main:
· Mitchell, Tom: Machine Learning McGraw-Hill Higher Education; New Edition (1 Oct 1997)
· Fayyad, U., Piatetsky-Shapiro, G. e Smyth, P.: From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37-54 (1996). Artigo disponível na Internet.
· Weiss, S. e Indurkhya, N.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann (1998)
· Almeida, P: Previsão do Comportamento de Séries Temporais Financeiras com Apoio de Conhecimento Sobre o Domínio. Ph.D. Thesis (in Portuguese), Universidade da Beira Interior, Portugal, 2003.
Secondary:
· Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2005)
· Larose, D.: Discovering Knowledge in Data: an Introduction to data Mining. John Wiley & Sons (2004)
Resources available in the internet (e.g.):
· http://en.wikipedia.org/wiki/Main_Page
Planned learning activities and teaching methods The teaching methodologies employed in this Curricular Unit allow the learning of the concepts and techniques of Machine Learning and Data Mining through exposition of theoretical subjects and use of online tutorials for study. The practical work, done in groups, allow the training/practicing the application of these theoretical aspects over real data, and will cover the discussion and learning about the most relevant practical problems that arise during these processes, and the main ways to surpass these problems. This way, the students will cover and experience the theoretical and practical subjects needed to reach the objectives of the Curricular Unit.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2017-07-01

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