INTRODUZIONE AL DATA MININGAcademic Year 2019/2020 - 3° Year - Curriculum A
Credit Value: 9
Scientific field: INF/01 - Informatics
Taught classes: 36 hours
Exercise: 36 hours
Term / Semester: 1°
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.
Detailed Course Content
- Probability and statistics
- Spectral Theory
- Introduction to R
- High Support Data Mining
- Low Support Data Mining
- Reccommendation Systems
- Clustering (hierarchical, k-means, density-based)
- Bayesian Classifiers
- Probabilistic Graphical Models
- Web Mining (PageRank, Hits, Books and Authors)
- Networks (Centrality, Clustering Coefficient)
- Introduction to Artificial Neural Networks and Deep Learning
- 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