Data Mining

Objectives

  1. Define key concepts in the fields of Data Mining, Machine Learning and related areas.
  2. Know appropriate classes of methods / algorithms, applications and programming libraries to solve the main problems in Data Mining.
  3. Apply software available for troubleshooting data mining issues, including the use of open source libraries 4. Build programs that can use available software libraries for the implementation of data mining pipelines.

Program

  1. Data transformations, kernels, data unbalance
  2. Unsupervised and semi-supervised data mining models: association rules, dimensionality reduction 3. Supervised models for data mining, model sets, their evaluation and optimization.
  3. Interpretability and visualization of learning models and the importance of attributes
  4. Mining of texts, time series and other sequential data.
  5. Graphing Mining
  6. Recommendation systems: collaborative and content filtering algorithms
  7. Evolutionary computing and other metaheuristics and their applications in data mining.
  8. Construction of data mining pipelines in various application areas.

Bibliography

M. Rocha, P. Cortez, J. Neves. Análise Inteligente de Dados - Algoritmos e Implementação em Java. FCA, 2008 I. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition, Morgan Kaufman, 2005. D. J. Hand, H. Manila, P. Smyth. Principles of Data Mining. MIT Press, 2001 De Jong K., Evolutionary Computation: A Unified Approach, MIT Press, 2006 I. Goodfellow, Y. Bengio, A. Courville. Deep learning. MIT Press, 2016.

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