OVERVIEW

In the information age any system or device generates some form of data for diagnostic purposes or analysis. The course details the basic techniques for collecting, processing and analyzing data in order to extract useful information and knowledge for decision making.

AIMS AND CONTENT

LEARNING OUTCOMES

The course provides advanced skills related to data analysis. It provides insights on data mining methodologies and the applications of these methodologies to knowledge extraction from data. The student will learn both the theoretical background and the practical issues for data analysis.

AIMS AND LEARNING OUTCOMES

The student will be able to apply the acquired skills to a case study by deriving the model of the phenomenon that generated the data under analysis.

TEACHING METHODS

The course consists of lectures and practical lab sessions using MATLAB.

SYLLABUS/CONTENT

1. Introduction
2. Data, Uncertainty and Learning Problems
3. ​Data preprocessing and Exploratory Data Analysis
4. Association Mining
5. Statistical inference
6. Naïve methods
7. Rule based methods
8. Linear methods
9. Kernel methods
10. Advances in kernel methods
11. Neural networks

RECOMMENDED READING/BIBLIOGRAPHY

C.C.Aggarwal, "Data Mining - The textbook", 2015

T.Hastie, R.Tibshirani, J.Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", 2009.

M.R.Berthold, C.Borgelt, F.Hoppner, F.Klawonn, "Guide to Intelligent Data Analysis", 2010.

TEACHERS AND EXAM BOARD

Exam Board

DAVIDE ANGUITA (President)

LUCA ONETO (President)

SANDRO RIDELLA

LESSONS

Class schedule

DATA ANALYSIS AND DATA MINING

EXAMS

EXAM DESCRIPTION

Oral

Exam schedule