1. Build a "dataset" from different data-storage, 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 de-normalization, 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.

Curricular Unit Form