SUMMARY

This course provides an introduction to Data Mining and an overview of all the most important algorithms used in this field. The course consists two sets of lectures. The first set, covering 24 hours, introduces the field of Data Mining and overviews all the main algorithms available in most commercial tools. The second set of lectures, covering 16 hours, focuses on specific application areas such as Text Mining, Bioinformatics, social networks, etc.

Students enrolled in the course of "Tecniche di Apprendimento Automatico per Applicazioni di Data Mining" need to attend only the first 20 hours of the course and can give the full exam in April/May during the first mid-term.

LIST OF TOPICS

• Introduction to Data Mining
• Understanding data and data representing
• Classification (decision trees, rules, Bayesian networks, etc.)
• Evaluation of classification algorithms
• Clustering
• Association rule mining
• Ensemble Methods (Bagging, Boosting, Random Forest)
• Support Vector Machines
• Text Mining
• Web Mining
• Data Exploration and Preprocessing
• Graph Mining and Social Networks
• Biological Data Analysis
• Data Streams

The exam consists of a written test. During the course the exam will be splitted between a first term around April/May and a second term in June. During the course, we will announce an optional project, involving the analysis of real-world data, that the students can take if they wish.

All the material from previous courses is available at http://www.pierlucalanzi.net/teaching/data-mining-and-text-mining/