Data Mining & Machine Learning

There are no significant specific knowledge base recommended for this curricular unit.

Classes will be taught on a theoretical-practical basis. The expository methodology will be used, supported by practical experimentation through application exercises, some on paper and others on computer. The most important practical experimentation will be achieved through a Data Mining project where students will choose a real organization or phenomenon, which they will analyze and use to apply concepts, tasks and techniques learned in theory. Classes will take place in person or, if necessary, at a distance, by synchronous classes through videoconference.

Many organizations from various industries implement data mining projects, which allow them to gain new knowledge,such as pattern behaviors and future trends, and thus decide more proactively. More robust models allow to analyzemore and more complex data, with faster and more accurate results and more easily identifying opportunities or risks.The Data Mining and Machine Learning curricular unit presents these two concepts, their differences,complementarities,motivations, and application domains. It is intended to provide students with knowledge in thesetwo areas, namely classification, forecasting, trend analysis (time series), grouping, summarization (and visualization)or association. It is also intended to exemplify in practice a set of techniques, such as decision trees, associationrules, linear regressions, artificial neural networks, sets and fuzzy logic or Bayes networks. Another objective is todevelop a data mining/machine learning project using real data.

1. Introduction to Data Mining and Machine Learning

2. Predictive (or supervised) activities:

2.1 Classification,
2.2 Forecasting,
2.3 Trend Analysis (Time Series)

3. Descriptive (or unsupervised) activities:

3.1 Grouping,
3.2 Summary (and visualization),
3.3 Association

4. CRISP-DM Methodology

5. Main Techniques:

5.1 Decision Trees,
5.2 Association Rules,
5.3 Linear Regression,
5.4 Artificial Neural Networks,
5.5 Fuzzy Sets and Fuzzy Logic,
5.6 Bayes Networks

NAO

Camilo, C., & Silva, J. (2009). Mineração de dados. Goiânia: Universidade Federal de Goiás.
Chakrabarti, S., Cox, E., Frank, E., Güting, R., Han, J., Jiang, X., Neapolitan, R. (2008). Data Mining: Know It All.Burlinghton: Morgan
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-By-Step Data Mining
Delen, D.. (2014). Real-World Data Mining: FT Press.
Larose, D. (2005). Discovering Knowledge in Data. Hoboken: John Wiley & Sons, Inc.
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. MIT press.
North, M. (2012). Data mining for the masses: A Global Text Project Book.
Santos, M., & Azevedo, C. (2005). Data Mining: Descoberta de Conhecimento em Bases de Dados. Lisboa: FCA.
Witten, I., & Frank, E. (2005). Data Mining: Practical machine learning tools and techniques. San Francisco: MorganKaufmann