LEARNING OUTCOMES

Develop the basic skills for extracting knowledge and knowledge from large data sets, in particular by forming an

• understanding of the value of data mining in solving real-world problems
• understanding of foundational concepts underlying data mining
• understanding of algorithms commonly used in data mining tools
• ability to apply data mining tools to real-world problems

AIMS AND LEARNING OUTCOMES

At the end of the course students will

• be able to understand and handle the main concepts and techniques of data mining
• be able to apply autonomously the main techniques of data mining to solve real-world problems
• to develop further knowledge about data mining techniques and applications

TEACHING METHODS

Combination of traditional lectures and lab sessions

SYLLABUS/CONTENT

First part: introduction to aata mining and applications in fraud detection
Introduction to Data Mining, Data science and big data analytics
Main techniques
The Data Mining Process - CRISP
Seven Class of Algorithms
            Supervised Learning – Classification
            Unsupervised Learnimg – Clustering
            Outliers detection
            Regression
            Reinforced Learning
            Ranking
            Deep Learning
Top ten data mining algorithms
Examples and application using WEKA
Application to marketing, finance and medicine
Big Data and Hadoop
The NOSql paradigm

Second part: Machine Learning Algorithms for Data mining
Introduction to Data Mining and Machine Learning.
Taxonomy of the Data Mining problems
Statistical Inference
Support Vector Machines (extension to kernels)
Support Vector Regression (extension to kernels)
K-means and Spectral Clustering
Decision Trees and Random Forests
Model Selection and Error Estimation

RECOMMENDED READING/BIBLIOGRAPHY

• Aggarwal, C- C. Data mining: the textbook. Springer, 2015.
• Shalev-Shwartz, S., and Shai B. D. Understanding machine learning: From theory to algorithms. Cambridge University Press, 2014.
• Ian H. Witten, Eibe Frank, Mark A. Hall (2000). Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems) ISBN-13: 978-0123748560. Disponibile presso il CSB di Ingegneria 006.312 WIT --> disponibile anche online a http://www.sciencedirect.com/science/book/9780123748560
• Clifton Phua, Vincent Lee, Kate Smith and Ross Gayler (2005). A Comprehensive Survey of Data Mining-based Fraud Detection Research, Computing Research Repository, abs/1009.6119. Disponibile online --> http://arxiv.org/abs/1009.6119
• N. Cristianini, J. Shawe-Taylor, An introduction to support Vector Machine and other kernel-based learning methods, Cambridge University Press, 2006 disponibile ING e ECO
• A. Ng, M. Jordan, Y. Weiss, On spectral clustering: Analysis and an algorithm, NIPS 2001. --> disponibile anche online a  http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf
• Dispense/Handouts