Instructors - Docenti:
Teaching assistant - Assistente:
Instructors:
1 Rizzi-Romano-Scigliuzzo 0,8134; 2 Criscolo-Quintini-Trafficante 0,80383; 3 Bazzali-Borghi-Giannella 0,79904; 3 Deidda-Policardo-Salamida 0,79904; 3 DelleMacchie-Iavarone-Rambelli 0,79904; 3 Kocan-Erdem 0,79904; 3 Stili-Strazzulla-Gaggioli 0,79904; 4 Calamia-Ortolani-Tardelli 0,79426; 5 Abedini-Baltakiene 0,78947; 5 Loconte-Spontella-Di Modugno 0,7894;
Abedini_Baltakiene, Alzetta_Miaschi_Semplici, Bambini_Catania_Incorvaia, Bazzali_Borghi_Giannella, Boncoraglio_Delicto_Veshi, Calamia_Ortolani_Tardelli, Criscolo_Quintini_Trafficante, Deidda_Policardo_Salamida, DelleMacchie_Iavarone_Rambelli, Donati, Dossena_Grossi_LaPerna, Fuccio_Furlan_LaPusata, Gentile_Miliani_Rossi, Giacalone_Montisci_Salerno, Kocan_Erdem, LaCroce, Loconte_Spontella_DiModugno, Rizzi_Romano_Scigliuzzo, Russo, Stili_Strazzulla_Gaggioli, Xu
… a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data. Hal Varian, Google’s chief economist, predicts that the job of statistician will become the “sexiest” around. Data, he explains, are widely available; what is scarce is the ability to extract wisdom from them.
Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.
La grande disponibilità di dati provenienti da database relazionali, dal web o da altre sorgenti motiva lo studio di tecniche di analisi dei dati che permettano una migliore comprensione ed un più facile utilizzo dei risultati nei processi decisionali. L'obiettivo del corso è quello di fornire un'introduzione ai concetti di base del processo di estrazione di conoscenza, alle principali tecniche di data mining ed ai relativi algoritmi. Particolare enfasi è dedicata agli aspetti metodologici presentati mediante alcune classi di applicazioni paradigmatiche quali il Basket Market Analysis, la segmentazione di mercato, il rilevamento di frodi. Infine il corso introduce gli aspetti di privacy ed etici inerenti all’utilizzo di tecniche inferenza sui dati e dei quali l’analista deve essere a conoscenza. Il corso consiste delle seguenti parti:
Classes - Lezioni
Giorno | Orario | Aula |
---|---|---|
Lunedì/Monday | 16:00 - 18:00 | Aula C |
Venerdì/Friday | 14:00 - 16:00 | Aula A1 |
Office hours - Ricevimento:
Classes - Lezioni
Day of week | Hour | Room |
---|---|---|
Monday | 9:00 - 11:00 | Room N1 |
Thursday | 9:00 - 11:00 | Room A1 |
Office hours - Ricevimento:
Day | Aula | Topic | Learning material | Instructor | |
---|---|---|---|---|---|
1. | 21.09.2015 16:00-18:00 | C | Canceled | - | |
2. | 25.09.2015 14:00-16:00 | A1 | Overview | 1.dm-overview.pdf | Pedreschi/Monreale |
3. | 28.09.2015 16:00-18:00 | C | Introduction | 2.dm_ml_introduction.pdf | Pedreschi |
4. | 02.10.2015 14:00-16:00 | A1 | Introduction | 2.dm_ml_introduction.pdf | Monreale |
5. | 05.10.2015 16:00-18:00 | C | Data Understanding | 3.dataunderstanding.pdf 3.data-understanting-appendix.pdf | Monreale |
6. | 09.10.2015 14:00-16:00 | A1 | Data Preparation | 4.data_preparation.pdf | Monreale |
7. | 12.10.2015 16:00-18:00 | C | Clustering analysis. Centroid-based methods. | dm2014_clustering_intro.pdf dm2014_clustering_kmeans.pdf | Monreale |
8. | 16.10.2015 14:00-16:00 | A1 | Clustering analysis. Hierarchical methods. Tutorial Knime | dm2014_clustering_hierarchical.pdf knime_slides_mains.pdf | Monreale |
9. | 19.10.2015 16:00-18:00 | C | Clustering Analysis. Density Based Clustering and Validation | dm2014_clustering_dbscan.pdf dm2014_clustering_validation.pdf | Monreale |
10. | 21.10.2015 16:00-18:00 | C | Exercises on Data Understanding. | exercises-dm1.pdf | Monreale |
11. | 23.10.2015 14:00-16:00 | A1 | Exercises on Clustering. | HC with Group Average exercises-clustering.pdf | Monreale/Guidotti |
12. | 26.10.2015 16:00-18:00 | C | Knime Exercises | datamanipulation.zip knime_clustering_iris.zip | Pedreschi/Guidotti |
13. | 30.10.2015 14:00-16:00 | A1 | R and Python Exercises | manipulation-clystering-r.zip manipulation-clustering-py.zip | Pedreschi/Guidotti |
02.11.2015-06.11.2015 | First Mid-term test: 6th November 14:00-16:00 Room A | ||||
14. | 09.11.2015 16:00-18:00 | C | Classification | chap4_basic_classification.pdf | Monreale |
15. | 13.11.2015 14:00-16:00 | A1 | Classification | Monreale | |
16. | 16.11.2015 16:00-18:00 | C | Classification | Monreale | |
17. | 20.11.2015 14:00-16:00 | A1 | Classification | Monreale | |
18. | 23.11.2015 16:00-18:00 | C | Exercises on Classification. Knime Exercises | knime_classification_iris.zip knime_classification_adult.zip knime_classification_over_adult.zip | Guidotti/Monreale |
19. | 27.11.2015 14:00-16:00 | A1 | Frequent Patterns & Association Rules | 4-5tdm-restructured_assoc.pdf | Monreale |
20. | 30.11.2015 16:00-18:00 | C | Canceled | ||
21. | 04.12.2015 14:00-16:00 | A1 | Canceled | ||
22. | 07.12.2015 16:00-18:00 | C | Canceled | Pedreschi | |
23. | 11.12.2015 14:00-16:00 | A1 | Exercises on Patterns. Knime Exercises | knime_pattern.zip | Guidotti / Pedreschi |
24. | 14.12.2015 16:00-18:00 | C | python-classification-pattern.zip r-classification-patterns.zip | Guidotti / Pedreschi | |
16.12.2015-18.12.2015 | Second Mid-term test |
Day | Aula | Topic | Learning material | Instructor | |
---|---|---|---|---|---|
1. | 22.02.2016 09:00-11:00 | N1 | Introduction + Sequential Patterns / 1 | sequential_patterns.pdf, textbook Ch. 7.4 | Nanni & Pedreschi |
2. | 25.02.2015 09:00-11:00 | A1 | Sequential Patterns / 2 | ||
3. | 29.02.2015 09:00-11:00 | A1 | Sequential Patterns / Exercises | Link to SPMF, a tool for seq. patterns and sample dataset. Exercises: Text 1 and Text 2 | |
4. | 03.03.2015 09:00-11:00 | A1 | Advanced Classification Methods / 1 | alternative_classification_1_dino_03.03.2016.pdf | Pedreschi |
5. | 07.03.2015 09:00-11:00 | A1 | Advanced Classification Methods / 2 | alternative_classification_2_dino_07.03.2016.pdf | Pedreschi |
6. | 10.03.2015 09:00-11:00 | A1 | Advanced Classification Methods / Tools and Exercises | exercises_classification.pdf sample_knime_workflows.zip | |
7. | 14.03.2015 09:00-11:00 | A1 | Advanced Classification Methods / Exercises | Exercises (also) on classification from 2014-15 | |
8. | 17.03.2015 09:00-11:00 | A1 | Time Series / 1 | time_series_from_keogh_tutorial.pdf | |
9. | 21.03.2015 09:00-11:00 | A1 | Time Series / 2 | ||
10. | 24.03.2015 09:00-11:00 | A1 | Time Series / Exercises | Some exercises from past exams: (Sequences and time series) (Classification) | |
25-29.03.2015 | EASTER HOLIDAYS | ||||
04.04.2015 09:00-13:00 | TBD | Midterm tests | |||
11. | 07.04.2015 09:00-11:00 | A1 | Case study: CRM - Customer Segmentation + CRISP-DM | Customer segmentation CRISP-DM | |
12. | 11.04.2015 09:00-11:00 | A1 | Case study: CRM - Churn Analysis | Intro_CRM Churn External_Churn | |
13. | 14.04.2015 09:00-11:00 | A1 | Case study: CRM - Promotions and Sophistication | Promotions Sophistication | |
14. | 18.04.2015 09:00-11:00 | A1 | Mobility Data Analysis / 1 | Preprocessing Patterns and models | |
15. | 21.04.2015 09:00-11:00 | A1 | Mobility Data Analysis / 2 | Individual/Collective models GSM_DM | |
16. | 28.04.2015 09:00-11:00 | A1 | Case study: Mobility Data Analysis | Case studies | |
17. | 02.05.2015 09:00-11:00 | A1 | Complements: Ethical Issues / 1 | slides | Monreale |
18. | 05.05.2015 09:00-11:00 | A1 | Complements: Ethical Issues / 2 | Monreale | |
19. | 09.05.2015 09:00-11:00 | A1 | Projects presentation | Projects | |
20. | 12.05.2015 09:00-11:00 | A1 | Complements: Outlier Detection | Slides from SDM2010 tutorial | |
21. | 16.05.2015 09:00-11:00 | A1 | Projects discussion |
The exam is composed of three parts:
Guidelines for the project are here.
The exam is composed of three parts:
Date | Hour | Place | Notes | Marks | |
---|---|---|---|---|---|
First Mid-term 2015 | Friday 06.11.2015 | 14.00 | Room A | Results | |
Second Mid-term 2015 | Wednesday 16.12.2015 | 11.00 | Room A1 | Results |
Date | Hour | Place | Notes | Marks | |
---|---|---|---|---|---|
Mid-term 2016 | Monday 04.04.2016 | 9.00 | Room A1 | Results |
Session | Date | Time | Room | Notes | Results |
---|---|---|---|---|---|
1. | Monday 18 January 2016 | 9.00 | A1 | In the same date we will define the dates for the oral exam. | |
2. | Monday 08 February 2016 | 9.00 | A1 | In the same date we will define the dates for the oral exam. | |
3. | Monday, 30 May 2016 | 9.00 | C | In the same date we will define the dates for the oral exam. | DM1: Written exam results DM2: Written exam results |
4. | Monday, 20 June 2016 | 9.00 | C | In the same date we will define the dates for the oral exam. | |
5. | Friday, 08 July 2016 | 9.00 | C | In the same date we will define the dates for the oral exam. | |
6. | Monday, 05 Sept 2016 | 9.00 | C | In the same date we will define the dates for the oral exam. |
Date | Time | Room | Notes | Results |
---|---|---|---|---|
6 November 2015 | 14:00-16:00 | Room A | ||
04 April 2016 | 9.00-13:00 | Room A1 |