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Délia Boino
Submitted by dboino on 23 March 2021
Intended learning outcomes

  1. Build a "dataset" from different datastorage, e.g., relational model or text on the Web, considering its structure and semantics in order to draw hypotheses and interpret results;
  2. Prepare data via denormalization, assembling and discretization;
  3. Explore the characteristics, options, benefits and limitations of supervised classification methods: a) with statistical support, b) based on the induction of decision trees, c) based on competitive learning;
  4. Introduce time series analysis; adapt datastet to apply (in this context) supervised classification methods
  5. Explore unsupervised methods based on instances;
  6. Explore the methods that search for association rules and highlight the difference between those methods and the ones related to classification and clustering;
  7. Evaluate learning via error estimation supported on the concepts of training, validation and testing sets; comparison of models and results presentation.


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