Scheda programma d'esame
FUNDAMENTALS OF DATA MINING AND MACHINE LEARNING
FRANCESCO MARCELLONI
Academic year2023/24
CourseBANKING FINANCE FINANCIAL MARKETS
Code1093I
Credits6
PeriodSemester 1
LanguageEnglish

ModulesAreaTypeHoursTeacher(s)
FUNDAMENTALS OF DATA MINING AND MACHINE LEARNINGING-INF/05LEZIONI0
FRANCESCO MARCELLONI unimap
Obiettivi di apprendimento
Learning outcomes
Conoscenze

Gli studenti che completeranno con successo l'insegnamento avranno una solida conoscenza delle principali tecniche per pre-processare i dati, frequent pattern mining, classificazione, predizione, clustering, e outlier detection. Questa conoscenza permetterà loro di affrontare diversi possibili problemi inerenti il data mining e di identificare la tecnica più adatta per risolverli.

Knowledge

The students who successfully complete the course will have a solid knowledge of the main techniques used in data preprocessing, frequent pattern mining, classification, prediction, clustering and outlier detection. This knowledge will allow them to tackle different types of data mining problem and to identify the most suitable technique for solving them.

Modalità di verifica delle conoscenze

Durante la verifica delle conoscenze, gli studenti devono dimostrare di aver appreso le diverse tecniche insegnate durante lo svolgimento del corso e devono essere capaci di identificare la soluzione più adatta per problemi di data mining specifici. 

I metodi sono:

  • esame orale
  • Prova pratica
Assessment criteria of knowledge

During the oral exam the student must be able to demonstrate his/her knowledge of the course material and be able to identify the most suitable solutions for specific data mining problems.

Methods:

  • Final oral exam
  • Practical test
Capacità

Al termine del corso, 

  • lo studente saprà affrontare i più comuni problemi di data mining, trovando le soluzioni più idonee per risolverli
  • lo studente saprà valutare e confrontare più soluzioni e scegliere la più adatta
Skills

At the end of the course,

  • the student will be able to tackle the most common problems in data mining, searching for the most suitable solution
  • the student will be able to evaluate and compare several possible solutions and to select the most effective
Modalità di verifica delle capacità

Lo studente dovrà risolvere un problema di data mining durante una prova pratica.

Assessment criteria of skills

The student will have to solve a data mining problem during a practical test.

Comportamenti

Lo studente potrà acquisire un metodo per affrontare problemi di data mining e per selezionare le migliori soluzioni da adottare

Behaviors

The student will acquire a method to deal with data mining problems and to select the most effective solution to be adopted

Modalità di verifica dei comportamenti

Durante le sessioni di laboratorio saranno valutati il grado di accuratezza e precisione delle attività svolte dallo studente

Assessment criteria of behaviors

During the laboratory sessions the degree of accuracy and precision of the activities carried out by the student will be evaluated

Prerequisiti (conoscenze iniziali)

Conoscenze di base di matematica

Conoscenze di programmazione 

Prerequisites

Basic knowledge of mathematics

Knowledge of programming 

Indicazioni metodologiche

Le lezioni verranno svolte frontalmente con l'ausilio di slide

Le esercitazioni verranno svolte in laboratorio con l'ausilio di slide e esempi di programmazione

L'intero corso è tenuto in Inglese

Teaching methods

Learning activities:

  • attending lectures
  • individual study
  • Laboratory work
  • Practical

Attendance: Advised

Teaching methods:

  • Lectures
  • Seminar
  • Task-based learning/problem-based learning/inquiry-based learning
  • laboratory
Programma (contenuti dell'insegnamento)

Data Preprocessing: data cleaning, integration, reduction, transformation and discretization.

Frequent pattern mining: basic concepts, A-priori algorithm, Pattern-Growth approach, vertical data format, pattern evaluation methods, constraint-based frequent pattern mining, colossal pattern.

Classification: basic concepts, decision tree induction, Bayes classification methods, rule-based classification, lazy learners, techniques for improving accuracy, model evaluation and selection.

Clustering: basic concepts, partitioning methods, hierarchical methods, density-based methods, grid-based methods, model evaluation and selection, clustering with constraints.

Outlier detection: statistical, proximity-based, clustering-based and classification-based approaches.

Syllabus

Data Preprocessing: data cleaning, integration, reduction, transformation and discretization.

Frequent pattern mining: basic concepts, A-priori algorithm, Pattern-Growth approach, vertical data format, pattern evaluation methods, constraint-based frequent pattern mining, colossal pattern.

Classification: basic concepts, decision tree induction, Bayes classification methods, rule-based classification, lazy learners, techniques for improving accuracy, model evaluation and selection.

Clustering: basic concepts, partitioning methods, hierarchical methods, density-based methods, grid-based methods, model evaluation and selection, clustering with constraints.

Outlier detection: statistical, proximity-based, clustering-based and classification-based approaches.

Bibliografia e materiale didattico

Slides

Libro: J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd ed., 2011 Papers on the different algorithms described during the course Slides of the lectures

Articoli forniti dal docente

Bibliography

Slides

Recommended book: J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd ed., 2011 Papers on the different algorithms described during the course Slides of the lectures

Papers provided by the teacher

Modalità d'esame

L'esame è composto da una prova pratica e una prova orale.

La prova pratica sarà sviluppata dando un problema allo studente che dovrà risolverlo utilizzando gli strumenti presentati a lezione e durante i laboratori

La prova orale consiste in un colloquio tra il candidato e il docente su alcune domande che possono essere anche assegnate in forma scritta al candidato.
La prova orale è superata  se il candidato mostra padronanza degli argomenti trattati, si esprime in modo chiaro e con terminologia corretta, mostra capacità di analisi e sintesi.

Assessment methods

The assessment method consists of a presentation of the project and an oral exam.

The practical test will be carried out by proposing a data mining problem to the student who will have to solve it using the tools presented in class and during the laboratories

The oral exam consists of a conversation between the candidate and the teacher on some questions that are assigned in writing to the candidate.
The oral exam is over if the candidate shows mastery of the topics covered, is expressed clearly and with correct terminology, shows the ability of analysis and synthesis.

Updated: 09/09/2023 12:56