Claudio
Sartori
Italy Full Professor of Machine Learning and Informatics University of Bologna Core Faculty
Claudio Sartori is a Machine Learning and Informatics Full Professor at the University of Bologna. At Bologna Business School, he is the Scientific Director of the Master in Data Science and Business Analytics and Consultant for activities in the Data Science area. He holds a Master Degree in Electronic Engineering (Italian title obtained in 1981). He was Visiting Professor at Technical University Federigo Santa Maria, Valparaiso, Chile. Claudio was also Responsible for the Observatory on Technological Innovation for the EULA-GTEC Erasmus + Project, whose goal is the design and launching of an Observatory or Antenna that allow perceiving training problems among managers (to improve training offers) and identifying SMEs demands, which are not clearly revealed (to improve demand identification). The Observatory also contribute to the employability of graduates creating links with the SMEs labour market. Claudio conducts Joint Research Cooperation with the Department of Informatics, Technical University Federico Santa Maria (UTFSM), Valparaiso, Chile and Universitè de Nice – Sophia Antipolis, Nice, France.
COURSES
Modern manufacturing processes exploit massively the digital technology, as is witnessed by the increasing interest towards the new operating paradigms known as “Industry 4.0”. The purpose of this module is to exploit the skills acquired in other modules, mainly “Data analysis”, “Data Mining” and “Operation Analytics”, to deal with the “Manufacturing data” and to extract information useful for increasing the effectiveness of manufacturing.
This course is a natural continuation of the Machine Learning course. It provides guidelines for running a Data Mining process and then discusses, with practical examples, the complete pipeline from data to machine learning concepts.
In particular, the following topics are covered:
- The functions of Data Mining.
- Supervised and unsupervised learning.
- Algorithms and methods for constructing classification models.
- Clustering algorithms.
- Algorithms for discovering association rules.
- Methods for evaluating the quality of data mining results.
Principles are introduced in class with presentation of slides and stimulating discussions with students. Methods are then applied with laboratory exercises.
Data Science and Business Analytics
Artificial Intelligence and Innovation Management
Introduction to the base principles and methods of Data Mining and Machine Learning, with particular reference to Classification, Clustering, Association Rules. Analysis of the major problems related to data quality and data transformation. Use of open source software to solve data mining and machine learning problems, with specific reference to datasets related to environment and sustainability
This course provides an introduction to the basic principles and methods of Data Mining and Machine Learning, with emphasis on Classification, Clustering, Association Rules, Outlier discovery. Analysis of the main problems related to data quality and data transformation. Python will be used as part of the course to solve machine learning problems.
Data Science and Business AnalyticsArtificial Intelligence and Innovation Management
Finance and Fintech
