ECTS - University of Pisa
| Modules | Area | Type | Hours | Teacher(s) | |
| ARTIFICIAL INTELLIGENCE I | ING-INF/05 | LEZIONI | 52 |
|
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.
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.
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:
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:
Al termine del corso,
At the end of the course,
Lo studente dovrà risolvere un problema di data mining durante una prova pratica.
The student will have to solve a data mining problem during a practical test.
Lo studente potrà acquisire un metodo per affrontare problemi di data mining e per selezionare le migliori soluzioni da adottare
The student will acquire a method to deal with data mining problems and to select the most effective solution to be adopted
Durante le sessioni di laboratorio saranno valutati il grado di accuratezza e precisione delle attività svolte dallo studente
During the laboratory sessions the degree of accuracy and precision of the activities carried out by the student will be evaluated
Conoscenze di base di matematica
Conoscenze di programmazione
Basic knowledge of mathematics
Knowledge of programming
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
Learning activities:
Attendance: Advised
Teaching methods:
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.
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.
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
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
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.
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.
