Ing - Civ (Mag.)(ord. 270) - MI (495) GEOINFORMATICS ENGINEERING - INGEGNERIA GEOINFORMATICA
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089167 - DATA MINING AND TEXT MINING (UIC 583)
Ing Ind - Inf (Mag.)(ord. 270) - MI (474) TELECOMMUNICATION ENGINEERING - INGEGNERIA DELLE TELECOMUNICAZIONI
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089167 - DATA MINING AND TEXT MINING (UIC 583)
Ing Ind - Inf (Mag.)(ord. 270) - MI (481) COMPUTER SCIENCE AND ENGINEERING - INGEGNERIA INFORMATICA
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089167 - DATA MINING AND TEXT MINING (UIC 583)
Ing Ind - Inf (Mag.)(ord. 270) - MI (487) MATHEMATICAL ENGINEERING - INGEGNERIA MATEMATICA
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089167 - DATA MINING AND TEXT MINING (UIC 583)
Obiettivi dell'insegnamento
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. An optional project will be available near the end of the course (around the last week of May-first week of June).
Risultati di apprendimento attesi
Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn to:
· Structure a data mining pipeline
· The fundamental characteristics of the most important algorithms used in all the major steps of the pipeline
· Identify architectural styles and patterns
Applying knowledge and understanding
Given specific data mining process, students will be able to:
· Analize and comment on specific architectural choices
· Highlight possible criticalities
· Identify existing biases
· Apply the theory to assess the reliability of the results produced
Making judgements
Given a data mining task, students will be able to:
· Analyze and understand the goals, assumptions and requirements associated with that task
· Select the best environment to implement each step of the data mining process
· Select the best infrastructure
Communication
Students will learn to:
· Analyze the design choices that a data analytics solution entails
· Present and critically discuss the results of a data mining process
Lifelong learning skills
Students will learn how to:
· Develop a project on real-world data and how to critically analyze a proposed solution and the result it produced
Ensemble Methods (Bagging, Boosting, Random Forest, Gradient Boosting)
Text Mining
Data Exploration and Preprocessing
Graph Mining and Social Networks
Prerequisiti
Students should have a basic knowledge of statistics and programming.
Modalità di valutazione
The exam consists of a written test at the end of the course. During the course, we will announce an optional project, involving the analysis of real-world data, that the students can take if they wish.
Type of assessment
Description
Dublin descriptor
Written test
Solution of numerical problems
· Computation of score functions used in data mining algorithms
· Execution of fundamental algorithms using small datasets
· Interpretation of code fragments to understand what they compute
Exercises focusing on design aspects
· Evaluation of trade-offs between different proposed solutions
· Definition of data mining pipelines for a given scenario
· Critical comparison of existing methods
1,2
1, 2, 3, 4, 5
Assessment of laboratorial artefacts
Assessment of the design of the data analytics pipeline and the experimental work developed by students either individually or in groups
Mohammed J. Zaki and Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Anno edizione: 2014, ISBN: 9780521766333 http://www.dataminingbook.info/ Note:
Inglese