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Machine Learning and Data Mining

Machine Learning and Data Mining

Code	11491
Year	1
Semester	S2
ECTS Credits	6
Workload	PL(30H)/T(30H)
Scientific area	Informatics
Entry requirements	NA
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
Language	Portuguese. Tutorial support is available in English.