INTRODUZIONE AL DATA MINING

General teaching training objectives in terms of expected learning outcomes.

Knowledge and understanding: The course aims to give the knowledge and basic and advanced skills to the analysis of data.

Applying knowledge and understanding: the student will acquire knowledge about the models and algorithms for analyzing data such as: mining high support, recommendation systems, search for similarities in high dimension, networks analysis, text mining, classification and clustering.

Making judgments: Through concrete examples and case studies, the student will be able to independently develop solutions to specific problems related to data analysis.

Communication skills: the student will acquire the necessary communication skills and expressive appropriateness in the use of technical language in the general area of ​​data analysis.

Learning skills: The course aims to provide students with the necessary theoretical and practical methods to deal independently and solve new problems that may arise during a work activity. For this purpose, different topics will be covered in class by involving students in the search for possible solutions to real problems, using benchmarks available in the literature.

• Background
  • Probability and statistics
  • Spectral Theory
  • Entropy
• Introduction to R
• High Support Data Mining
• Low Support Data Mining
• Reccommendation Systems
• Clustering (hierarchical, k-means, density-based)
• Classification
• Bayesian Classifiers
• Probabilistic Graphical Models
• Web Mining (PageRank, Hits, Books and Authors)
• Networks (Centrality, Clustering Coefficient)

• Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman, http://www.mmds.org
• Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber, The Morgan Kaufmann Series in Data Management Systems
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie,Robert Tibshirani, Jerome Friedman, Springer