Queste sono le differenze tra la revisione selezionata e la versione attuale della pagina.
Entrambe le parti precedenti la revisione Revisione precedente Prossima revisione | Revisione precedente | ||
dm:start [15/10/2019 alle 17:21 (5 anni fa)] Anna Monreale [First part of course, first semester (DM1 - Data mining: foundations & DM - Data Mining)] |
dm:start [26/03/2024 alle 17:16 (2 giorni fa)] (versione attuale) Riccardo Guidotti [Second Semester (DM2 - Data Mining: Advanced Topics and Applications)] |
||
---|---|---|---|
Linea 9: | Linea 9: | ||
ga(' | ga(' | ||
ga(' | ga(' | ||
- | ga(' | + | ga(' |
- | | + | |
ga(' | ga(' | ||
- | ga(' | + | ga(' |
setTimeout(" | setTimeout(" | ||
</ | </ | ||
<!-- End Google Analytics --> | <!-- End Google Analytics --> | ||
+ | <!-- Global site tag (gtag.js) - Google Analytics --> | ||
+ | <script async src=" | ||
+ | < | ||
+ | window.dataLayer = window.dataLayer || []; | ||
+ | function gtag(){dataLayer.push(arguments); | ||
+ | gtag(' | ||
+ | |||
+ | gtag(' | ||
+ | </ | ||
<!-- Capture clicks --> | <!-- Capture clicks --> | ||
< | < | ||
Linea 42: | Linea 50: | ||
</ | </ | ||
</ | </ | ||
- | ====== Data Mining A.A. 2019/20 ====== | + | ====== Data Mining A.A. 2023/24 ====== |
- | ===== DM 1: Foundations of Data Mining (6 CFU) ===== | + | ===== DM1 - Data Mining: Foundations |
- | Instructors | + | Instructors: |
* **Dino Pedreschi** | * **Dino Pedreschi** | ||
- | * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa | + | * KDDLab, Università di Pisa |
* [[http:// | * [[http:// | ||
* [[dino.pedreschi@unipi.it]] | * [[dino.pedreschi@unipi.it]] | ||
- | + | * **Riccardo Guidotti** | |
- | | + | |
+ | * [[https:// | ||
+ | * [[riccardo.guidotti@di.unipi.it]] | ||
- | ===== DM 2: Advanced topics on Data Mining and case studies | + | Teaching Assistant |
+ | * **Andrea Fedele** | ||
+ | * KDDLab, Università di Pisa | ||
+ | * [[https:// | ||
+ | * [[andrea.fedele@phd.unipi.it]] | ||
+ | ===== DM2 - Data Mining: Advanced Topics | ||
Instructors: | Instructors: | ||
- | * **Mirco Nanni, Dino Pedreschi** | + | * **Riccardo Guidotti** |
- | * KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa | + | * KDDLab, Università di Pisa |
- | * [[http://www-kdd.isti.cnr.it]] | + | * [[https:// |
- | * [[mirco.nanni@isti.cnr.it]] | + | * [[riccardo.guidotti@di.unipi.it]] |
- | * [[dino.pedreschi@unipi.it]] | + | |
- | ===== DM: Data Mining (9 CFU) ===== | + | Teaching Assistant |
+ | * **Andrea Fedele** | ||
+ | * KDDLab, Università di Pisa | ||
+ | * [[https:// | ||
+ | * [[andrea.fedele@phd.unipi.it]] | ||
+ | * Meeting: https:// | ||
+ | ====== News ====== | ||
- | Instructors: | + | * **[19.01.2024]** DM2 Lectures will start on Mon 19/02, only for that lecture the time will be 14-16 instead of 9-11. |
- | | + | |
- | * KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa | + | * [20.09.2023] Recordings of the lectures can be found on the web pages of the course for the years 2020/2021 and 2021/2022 (see links at the bottom of this page) |
- | * [[http://www-kdd.isti.cnr.it]] | + | |
- | * [[mirco.nanni@isti.cnr.it]] | + | |
- | * [[dino.pedreschi@unipi.it]] | + | * [11.09.2023] Lectures will be in presence only. Registrations of the lectures of past years can be found at the bottom of this web page. |
- | * [[anna.monreale@unipi.it]] | + | |
+ | | ||
+ | ====== Learning Goals ====== | ||
+ | * DM1 | ||
+ | * Fundamental concepts of data knowledge and discovery. | ||
+ | * Data understanding | ||
+ | * Data preparation | ||
+ | * Clustering | ||
+ | * Classification | ||
+ | * Pattern Mining and Association Rules | ||
+ | * Sequential Pattern Mining | ||
+ | * DM2 | ||
+ | * Outlier Detection | ||
+ | * Dimensionality Reduction | ||
+ | * Regression | ||
+ | * Advanced Classification and Regression | ||
+ | * Time Series Analysis | ||
+ | * Transactional Clustering | ||
+ | * Explainability | ||
- | ====== | + | ====== |
- | * **[03.10.2019] Please, fill the [[https:// | + | |
- | * [26.09.2019] Global Climate Strike: teachers of DM course tomorrow Friday September 27 will join the Global Climate strike, so tomorrow the lecture is suppressed. | + | |
- | * [18.09.2019] Event: " | + | |
- | + | ||
- | ====== Learning goals -- Obiettivi del corso ====== | + | |
- | ** ... a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/ | + | ===== DM1 ===== |
- | + | ||
- | //Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.// | + | |
- | La grande disponibilità di dati provenienti da database relazionali, | + | **Classes** |
- | - i concetti di base del processo di estrazione della conoscenza: studio e preparazione dei dati, forme dei dati, misure e similarità dei dati; | + | |
- | - le principali tecniche di datamining (regole associative, | + | |
- | - alcuni casi di studio nell’ambito del marketing e del supporto alla gestione clienti, del rilevamento di frodi e di studi epidemiologici. | + | |
- | - l’ultima parte del corso ha l’obiettivo di introdurre gli aspetti di privacy ed etici inerenti all’utilizzo di tecniche inferenza sui dati e dei quali l’analista deve essere a conoscenza | + | |
- | + | ||
- | ===== Reading about the "data scientist" | + | |
- | + | ||
- | * Data, data everywhere. The Economist, Feb. 2010 {{: | + | |
- | * Data scientist: The hot new gig in tech, CNN & Fortune, Sept. 2011 [[http:// | + | |
- | * Welcome to the yotta world. The Economist, Sept. 2011 {{: | + | |
- | * Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, Sept 2012 [[http:// | + | |
- | * Il futuro è già scritto in Big Data. Il SOle 24 Ore, Sept 2012 [[http:// | + | |
- | * Special issue of Crossroads - The ACM Magazine for Students - on Big Data Analytics {{: | + | |
- | * Peter Sondergaard, | + | |
- | + | ||
- | * Towards Effective Decision-Making Through Data Visualization: | + | |
- | ====== Hours - Orario e Aule ====== | + | |
- | + | ||
- | ===== DM1 & DM ===== | + | |
- | + | ||
- | **Classes | + | |
^ Day of Week ^ Hour ^ Room ^ | ^ Day of Week ^ Hour ^ Room ^ | ||
- | | | + | | Monday |
- | | | + | | Wednesday |
- | | Venerdì/ | + | |
**Office hours - Ricevimento: | **Office hours - Ricevimento: | ||
- | * Prof. Pedreschi: Lunedì/Monday | + | * Prof. Pedreschi |
- | * Prof. Monreale: | + | * Monday |
+ | * Online | ||
+ | * Prof. Guidotti | ||
+ | * Tuesday 16:00 - 18:00 or Appointment by email | ||
+ | * Room 363 Dept. of Computer Science or MS Teams | ||
| | ||
Linea 123: | Linea 136: | ||
- | **Classes | + | **Classes** |
- | ^ Day of week | + | ^ Day of Week |
- | | Thursday | + | | |
- | | Friday | + | | |
- | **Office | + | **Office |
+ | |||
+ | * Tuesday 15.00-17.00 or Appointment by email | ||
+ | * Room 363 Dept. of Computer Science or MS Teams | ||
- | * Nanni : appointment by email, c/o ISTI-CNR | ||
====== Learning Material -- Materiale didattico ====== | ====== Learning Material -- Materiale didattico ====== | ||
Linea 138: | Linea 153: | ||
* Pang-Ning Tan, Michael Steinbach, Vipin Kumar. **Introduction to Data Mining**. Addison Wesley, ISBN 0-321-32136-7, | * Pang-Ning Tan, Michael Steinbach, Vipin Kumar. **Introduction to Data Mining**. Addison Wesley, ISBN 0-321-32136-7, | ||
* [[http:// | * [[http:// | ||
- | * I capitoli | + | * I capitoli |
* Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. **GUIDE TO INTELLIGENT DATA ANALYSIS.** Springer Verlag, 1st Edition., 2010. ISBN 978-1-84882-259-7 | * Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. **GUIDE TO INTELLIGENT DATA ANALYSIS.** Springer Verlag, 1st Edition., 2010. ISBN 978-1-84882-259-7 | ||
* Laura Igual et al.** Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications**. 1st ed. 2017 Edition. | * Laura Igual et al.** Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications**. 1st ed. 2017 Edition. | ||
Linea 144: | Linea 159: | ||
- | ===== Slides | + | ===== Slides ===== |
- | * The slides used in the course will be inserted in the calendar after each class. Most of them are part of the the slides provided by the textbook' | + | * The slides used in the course will be inserted in the calendar after each class. Most of them are part of the slides provided by the textbook' |
- | ===== Past Exams ===== | ||
- | * Some text of past exams on **DM1 (6CFU)**: | + | |
+ | ===== Software===== | ||
- | * {{ :dm:2017-1-19.pdf |}}, {{ :dm:2017-9-6.pdf |}}, {{ :dm:2016-05-30-dm1-seconda.pdf |}} | + | * Python - Anaconda (>3.7): Anaconda is the leading open data science platform powered by Python. [[https://www.anaconda.com/ |
+ | * Scikit-learn: python library with tools for data mining and data analysis [[http://scikit-learn.org/ | ||
+ | * Pandas: pandas is an open source, BSD-licensed library providing high-performance, | ||
- | * Some solutions of past exams containing exercises on KNN and Naive Bayes classifiers | + | Other softwares for Data Mining |
- | * {{ :dm:dm2_exam.2017.06.13_solutions.pdf |}}, {{ :dm:dm2_exam.2017.07.04_solutions.pdf |}}, {{ :dm: | + | |
+ | * [[http://www.cs.waikato.ac.nz/ | ||
+ | * Didactic Data Mining [[http:// | ||
+ | |||
+ | ====== Class Calendar (2023/2024) ====== | ||
- | * Some exercises | + | ===== First Semester |
- | * {{ : | + | |
+ | ^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^ | ||
+ | |01.| 18.09.2023 | 11-13 |C1| Overview, Introduction | {{ : | ||
+ | | | 20.09.2023 | 11-13 | | No Lecture | | | | ||
+ | |02.| 25.09.2023 | 11-13 |C1| Lab. Introduction to Python | {{ : | ||
+ | |03.| 27.09.2023 | 11-13 |C1| Lab. Data Understanding | {{ : | ||
+ | |04.| 02.10.2023 | 11-13 |C1| Data Understanding | {{ : | ||
+ | |05.| 04.10.2023 | 11-13 |C1| Data Understanding & Preparation | {{ : | ||
+ | |06.| 09.10.2023 | 11-13 |C1| Data Preparation & Data Similarity | {{ : | ||
+ | |07.| 11.10.2023 | 11-13 |C1| Data Similarity & Lab. Data Understanding | {{ : | ||
+ | |08.| 16.10.2023 | 11-13 |C1| Introduction to Clustering, K-Means | {{ : | ||
+ | |09.| 18.10.2023 | 11-13 |C1| Clustering Validation, Hierarchical Clustering | {{ : | ||
+ | |10.| 23.10.2023 | 11-13 |C1| Density-based Clustering | {{ : | ||
+ | |11.| 25.10.2023 | 11-13 |C1| Lab. Clustering | {{ : | ||
+ | |12.| 30.10.2023 | 11-13 |C1| Ex. Clustering | {{ : | ||
+ | | | 01.11.2023 | 11-13 | | No Lecture | | | | ||
+ | |13.| 06.11.2023 | 11-13 |C1| Intro Classification, | ||
+ | |14.| 08.11.2023 | 11-13 |C1| Naive Bayes, Exercises | {{ : | ||
+ | |15.| 13.11.2023 | 11-13 |C1| Model Evaluation | {{ : | ||
+ | |16.| 15.11.2023 | 11-13 |C1| Model Evaluation Exercises & Lab | {{ : | ||
+ | | | 20.11.2023 | 11-13 | | No Lecture | | | | ||
+ | |17.| 22.11.2023 | 11-13 |C1| Decision Tree Classifier | {{ : | ||
+ | |18.| 27.11.2023 | 11-13 |C1| Decision Tree Classifier | {{ : | ||
+ | |19.| 29.11.2023 | 11-13 |C1| Exercises and Lab. Decision Tree Classifier | {{ : | ||
+ | |20.| 04.12.2023 | 11-13 |C1| Decision Tree Classifier, Exercises and Lab | {{ : | ||
+ | |21.| 06.12.2023 | 11-13 |C1| Intro Regression & Lab. Regression | {{ : | ||
+ | |22.| 11.12.2023 | 11-13 |C1| Into Pattern Mining and Apriori | {{ : | ||
+ | |23.| 13.12.2023 | 16-18 |C1| Apriori & Lab. Pattern Mining | {{ : | ||
+ | |24.| 18.12.2023 | 11-13 |C| FP-Growth and Exercises | {{ : | ||
+ | ===== Second Semester (DM2 - Data Mining: Advanced Topics and Applications) ===== | ||
- | * Some very old exercises (part of them with solutions) are available here, most of them in Italian, not all of them on topics covered in this year program: | + | ^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^ |
- | | + | |01.| 19.02.2024 | 14-16 |C| Overview, Rule-based Models | {{ :dm: |
- | | + | | | 21.02.2024 | | | No Lecture | | | |
- | * {{:dm:verifica.2008.04.03.pdf|Verifica 3 aprile 2008}} (e {{:dm:soluzioni.2008.04.03.pdf|Soluzioni}}), {{:dm:dm-tdm.appello_2008_07_18_parte1.pdf|Verifica | + | | | 26.02.2024 | | | No Lecture | | | |
- | * {{:dm:appello.2010.06.01_soluzioni.pdf| Exam with solution 2010-06-01}} {{:dm:appello.2010.06.22_soluzioni.pdf|Exam with solution 2010-06-22}} {{:dm:appello.2010.09.09_soluzioni.pdf|Exam with solution 2010-09-09}}{{:dm:appello.2010.07.13_soluzioni.pdf| Exam with solution 2010-07-13}} | + | |02.| 19.02.2024 | 11-13 |C| Sequential Pattern Mining | {{ :dm:16_dm2_sequential_pattern_mining_2023_24.pdf | Sequential Pattern Mining}}, {{ :dm:GSP.zip | GSP}} | Guidotti| |
+ | |03.| 04.03.2024 | 9-11 |C| Sequential Pattern Mining | {{ : | ||
+ | |04.| 06.03.2024 | 11-13 |C| Transactional Clustering | ||
+ | |05.| 11.03.2024 | 9-11 |C| Time Series Similarity | {{ : | ||
+ | |06.| 13.03.2024 | 11-13 |C| Time Series Approximation | {{ : | ||
+ | |07.| 18.03.2024 | 9-11 |C| Time Series Clustering & Motifs| | ||
+ | |08.| 20.03.2024 | 11-13 |C| Time Series Classification | {{ :dm:21_dm2_time_series_classification_2023_24.pdf | Time Series Classification}}, {{ :dm:dm2_lab04_classification.zip | TS_Classification}} | Guidotti| | ||
+ | |09.| 25.03.2024 | ||
+ | |10.| 27.03.2024 | 11-13 |C| Dimensionality Reduction | {{ : | ||
+ | ====== Exams ====== | ||
- | ===== Data mining software===== | + | ** How and Where: ** |
+ | The exam will take place in oral mode only at the teacher' | ||
+ | The exam will be held online on the 420AA Data Mining course channel only at the request of the | ||
+ | student in accordance with current legislation. | ||
- | | + | ** When: ** |
- | * Python - Anaconda (3.7 version!!!): | + | The dates relating to the start of the three exams are/will be published on the online |
- | * Scikit-learn: | + | https://esami.unipi.it/. Within each session, we will identify dates and slots in order to distribute |
- | * Pandas: pandas is an open source, BSD-licensed library providing high-performance, | + | various orals. The dates and slots to take the exam will be published on the course |
- | * [[http:// | + | May. Each student must also register on https://esami.unipi.it/. The examination can only be carried out after the delivery of the project. The project must be delivered one week before when you want to take the exam. Group oral discussions will be preferred |
- | + | In the event that the oral exam is not passed, it will not be possible to take it for 20 days. If the project is not considered sufficient, it must be carried out again on a new dataset or a very updated version of the current one. | |
- | ====== Class calendar - Calendario delle lezioni (2019/2020) ====== | + | |
- | ===== First part of course, | + | ** What: ** |
+ | The oral test will evaluate the practical understanding | ||
+ | - Understanding of the theoretical aspects of the topics addressed during the course. The student may be required to write on formulas or pseudocode. During the explanations, the student can use pen and paper. | ||
+ | | ||
+ | | ||
+ | questionable steps or choices. | ||
- | ^ ^ Day ^ Topic ^ Learning material ^ Instructor ^ | + | ** Final Mark: ** for 12-credit exam, the final mark will be obtained as the |
- | |1.| 16.09 14:00-16:00 | Overview. Introduction to KDD | {{ : | + | average mark of DM1 and DM2. |
- | | | 18.09 16:00-18:00 | Lecture canceled | + | |
- | |2.| 20.09 11:00-13:00 | Introduction to KDD: technologies, Application and Data | | Pedreschi | + | |
- | |3.| 23.09 14:00-16:00 | Data Understanding (from Bertold book!) | + | |
- | |4.| 25.09 16:00-18:00 | Data Preparation | + | |
- | | | 27.09 11:00-13:00 | Climate Strike | + | |
- | |5.| 30.09 14:00-16:00 | Introduction to Python. | + | |
- | |6.| 02.10 16:00-18:00 | Clustering: Introduction + Centroid-based clustering, K-means | {{ : | + | |
- | |7.| 04.10 11:00-13:00 | Lab: Data Understanding & Preparation in Knime | Knime: {{ : | + | |
- | |8.| 07.10 14:00-16:00 | Lab: DU Python + Project presentation | + | |
- | |9.| 09.10 16:00-18:00 | Clustering: K-means + Hierarchical | + | |
- | |10.| 11.10 11:00-13:00 | Suppressed for Internet festival | | Pedreschi | | + | |
- | |11.| 14.10 14:00-16:00 | Clustering: DBSCAN & VALIDITY | + | |
- | |12.| 16.10 16:00-18:00 | Exercises on Clustering| | + | |
- | |13.| 18.10 11:00-13:00 | Lab: Clustering | + | |
- | |14.| 21.10 14:00-16:00 | Classification | + | |
- | |15.| 23.10 16:00-18:00 | Classification | + | |
- | |16.| 25.10 11:00-13:00 | Classification | + | |
- | |17.| 28.10 14:00-16:00 | LAB: Classificazione | + | |
- | |18.| 30.10 16:00-18:00 | Exercises Classification + Discussion Clustering | + | |
- | |19.| 04.11 11:00-13:00 | Pattern Mining | + | |
- | |20.| 06.11 16:00-18:00 | Pattern Mining | + | |
- | | | 08-14.11 | + | |
- | |21.| 15.11 11:00-13:00 | Exercises and Lab on Pattern Mining | + | |
- | | | 18.11 14:00-16:00 | Suppressed | + | |
- | | | 20.11 16:00-18:00 | Suppressed | + | |
- | |22.| 22.11 11:00-13:00 | Exercises Classification| | + | |
- | | | + | |
- | ===== Second part of course, second semester (DMA - Data mining: advanced topics and case studies) | + | ===== Exam Booking Periods ===== |
+ | * Exam portal link: [[https:// | ||
+ | * 1st Appello: from 09/01/2024 to 31/ | ||
+ | * 2nd Appello: from 01/02/2024 to 17/ | ||
+ | * 3rd Appello: | ||
+ | * 4th Appello: | ||
+ | * 5th Appello: | ||
+ | * 6th Appello: | ||
+ | |||
+ | ===== Exam Booking Agenda ===== | ||
+ | * 1st Appello - DM1: https:// | ||
+ | * 2nd Appello - DM1: https:// | ||
+ | * 3rd Appello: | ||
+ | * 4th Appello: | ||
+ | * 5th Appello: | ||
+ | * 6th Appello: | ||
- | ^ ^ Day ^ Room (Aula) ^ Topic ^ Learning material ^ Instructor (default: Nanni)^ | + | **Do not forget |
- | |1.| 21.02.2019 14:00-16:00 | A1 | Introduction + Sequential patters/1 | {{ : | + | ===== Exam DM1 ====== |
- | |2.| 22.02.2019 16:00-18:00 | C1 | Sequential patterns/ | + | |
- | |3.| 01.03.2019 16:00-18:00 | C1 | Sequential patterns/3 | {{ : | + | |
- | |4.| 07.03.2019 14:00-16:00 | A1 | Sequential patterns/4 | Sequential pattern tools: Link to [[http:// | + | |
- | |5.| 08.03.2019 16:00-18:00 | C1 | Time series/ | + | |
- | |6.| 14.03.2019 14:00-16:00 | A1 | Time series/2 | [[https:// | + | |
- | |7.| 15.03.2019 16:00-18:00 | C1 | Time series/3 | | | | + | |
- | |8.| 21.03.2019 14:00-16:00 | A1 | Time series/4 | {{ : | + | |
- | |9.| 22.03.2019 16:00-18:00 | C1 | Time series/5 | | | | + | |
- | |10.| 28.03.2019 14:00-16:00 | A1 | Exercises for mid-term exam | {{ : | + | |
- | |11.| 29.03.2019 16:00-18:00 | C1 | Exercises for mid-term exam | {{ : | + | |
- | | | 04.04.2019 16:00-18:00 | A1 + E | **mid-term exam** | | | | + | |
- | |11.| 11.04.2019 14:00-16:00 | A1 | Classification: | + | |
- | |12.| 12.04.2019 16:00-18:00 | C1 | Classification: | + | |
- | | | < | + | |
- | |13.| 03.05.2019 16:00-18:00 | C1 | Classification: | + | |
- | |14.| 09.05.2019 14:00-16:00 | A1 | Classification: | + | |
- | |15.| 10.05.2019 16:00-18:00 | C1 | Classification: | + | |
- | |16.| 16.05.2019 14:00-16:00 | A1 | Classification: | + | |
- | |17.| 17.05.2019 16:00-18:00 | C1 | Classification: | + | |
- | |18.| 23.05.2019 14:00-16:00 | A1 | Exercises + Outlier detection/ | + | |
- | |19.| 24.05.2019 16:00-18:00 | C1 | Outlier detection/ | + | |
- | |< | + | |
- | | | 06.06.2019 16:00-18:00 | E (+A1) | **mid-term exam** | {{ : | + | |
- | ====== Exams ====== | + | |
- | ===== Exam DM part I (DMF) ====== | + | The exam is composed of two parts: |
- | The exam is composed of three parts: | + | * An **oral |
- | * A **written exam**, with exercises | + | * A **project**, that consists in exercises |
+ | |||
+ | * **Dataset** | ||
+ | - Assigned: 25/ | ||
+ | - MidTerm Submission: 15/11/2023 (+0.5) (half project required, i.e., Data Understanding & Preparation and Clustering) | ||
+ | - Final Submission: 31/12/2023 (+0.5) one week before the oral exam (complete project required). | ||
+ | - Dataset: {{ :dm:std.zip | STD}} | ||
- | | + | ** DM1 Project Guidelines |
+ | See {{ :dm:dm1_project_guidelines_23_24.pdf | Project Guidelines}}. | ||
- | * A **project** consists in exercises that require the use of data mining tools for analysis of data. Exercises include: data understanding, | ||
- | Tasks of the project: | ||
- | - ** Data Understanding: | ||
- | - ** Clustering analysis: ** Explore the dataset using various clustering techniques. Carefully describe your's decisions for each algorithm and which are the advantages provided by the different approaches. (see Guidelines for details) | ||
- | - ** Classification: | ||
- | - ** Association Rules: ** Explore the dataset using frequent pattern mining and association rules extraction. Then use them to predict a variable either for replacing missing values or to predict target variable. (see Guidelines for details) | ||
- | * Project 1 | ||
- | - Dataset: **Carvana Data** | ||
- | - Assigned: 07/10/2019 | ||
- | - Deadline: 05/ | ||
- | - Link: https:// | ||
+ | |||
+ | ===== Exam DM2 ====== | ||
+ | |||
+ | The exam is composed of two parts: | ||
+ | * An **oral exam**, that includes: (1) discussing the project report; (2) discussing topics presented during the classes, including the theory and practical exercises. | ||
- | **Guidelines for the project | + | |
- | ===== Exam DM part II (DMA) ====== | + | * **Dataset** |
+ | - Assigned: 19/ | ||
+ | - MidTerm Submission: 30/ | ||
+ | - Final Submission: one week before the oral exam (complete project required, also with DL-based models for TS classification). | ||
+ | - Dataset: [[https:// | ||
- | The exam is composed of three parts: | + | ** DM2 Project Guidelines ** |
+ | See {{ :dm: | ||
- | * A **written exam**, with exercises and questions about methods and algorithms presented during the classes. It can be substitute with the first and second mid-term tests of April and June. | ||
- | *< | ||
- | * An **oral exam**, that includes: (1) discussing the project report with a group presentation; | ||
- | * A **project**, | ||
- | * **Dataset**: | ||
- | * **Task 1: Time series**: Consider only attribute " | ||
- | * **Task 2: Sequential patterns**: discover contiguous sequential patterns of at least length 4. Before that, time series should be discretized in some way. | ||
- | * **Task 3: | ||
- | * **Task 4: Outlier detection**: | ||
- | ====== Appelli di esame ====== | + | ===== Past Exams ===== |
+ | * Past exams texts can be found in old pages of the course. Please do not consider these exercises as a unique way of testing your knowledge. Exercises can be changed and updated every year and will be published together with the slides of the lectures. | ||
- | ===== Mid-term exams ===== | + | ===== Reading About the "Data Scientist" |
- | ^ ^ Date ^ Hour ^ Place ^ Notes ^ Marks ^ | + | ** ... a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/ |
- | | DM1: First Mid-term 2018 | 30.10.2018 | 11-13 | Room C1, L1, N1 | Please, use the system for registration: | + | |
- | | DM1: Second Mid-term 2018 | 18.12.2018| 11-13 | Room C1, L1, N1 | Please, use the system for registration: | + | |
- | | DM2: First Mid-term 2019 | 04.04.2019 | 16-18 | Room A1, E | Please, use the system for registration: | + | |
- | | DM2: Second Mid-term 2019 | 06.06.2019 | 16-18 | Room E \\ (+ A1 if needed) | Please, use the system for registration: | + | |
- | ===== Appelli regolari / Exam sessions ===== | + | |
- | ^ Session ^ Date ^ Time ^ Room ^ Notes ^ Marks ^ | + | |
- | |1.|16.01.2019| 14:00 - 18:00| Room E | | | | + | |
- | |2.|06.02.2019| 14:00 - 18:00| Room E | | | | + | |
- | |3.|19.06.2019| 09:00 - 13:00| Room A1 | Oral Exam on DM1 within 15 July. If you cannot do within that date you can do the oral exam on September.| {{ : | + | |
- | |4.|10.07.2019| 09:00 - 13:00| Room A1 |Oral Exam on DM1 within 15 July. If you cannot do within that date you can do the oral exam on September. | {{ : | + | |
- | ===== Appelli straordinari A.A. 2017/18 / Extra sessions A.A. 20167/ | + | |
- | ^ Date ^ Time ^ Room ^ Notes ^ Results ^ | + | //Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.// |
+ | |||
+ | * Data, data everywhere. The Economist, Feb. 2010 {{: | ||
+ | * Data scientist: The hot new gig in tech, CNN & Fortune, Sept. 2011 [[http:// | ||
+ | * Welcome to the yotta world. The Economist, Sept. 2011 {{: | ||
+ | * Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, Sept 2012 [[http:// | ||
+ | * Il futuro è già scritto in Big Data. Il SOle 24 Ore, Sept 2012 [[http:// | ||
+ | * Special issue of Crossroads - The ACM Magazine for Students - on Big Data Analytics {{: | ||
+ | * Peter Sondergaard, | ||
+ | * Towards Effective Decision-Making Through Data Visualization: | ||
====== Previous years ===== | ====== Previous years ===== | ||
- | * [[dm.2018-19]] | + | * [[dm.2022-23ds]] |
- | * | + | * [[dm.2021-22ds]] |
+ | * [[dm.2020-21]] | ||
+ | * [[dm.2019-20]] | ||
+ | | ||
+ | | ||
* [[dm.2016-17]] | * [[dm.2016-17]] | ||
* [[dm.2015-16]] | * [[dm.2015-16]] | ||
Linea 309: | Linea 338: | ||
* [[dm.2012-13]] | * [[dm.2012-13]] | ||
* [[dm.2011-12]] | * [[dm.2011-12]] | ||
- | * [[dm.2010-11]] | + | |
- | * [[dm.2009-10]] | + | |
- | * [[dm.2008-09]] | + | |
- | * [[dm.2007-08]] | + | |
- | * [[dm.2006-07]] | + | |
- | * [[PhDWorkshop2011]] | + | |
- | * [[SNA.Ingegneria2011]] | + | |
- | * [[SNA.IMT.2011]] | + | |
- | * [[MAINS.SANTANNA.2011-12]] | + | |
- | * [[MAINS.SANTANNA.DM4CRM.2012]] | + | |
- | * [[MAINS.SANTANNA.DM4CRM.2016]] | + | |
- | * [[MAINS.SANTANNA.DM4CRM.2017 | Data Mining for Customer Relationship Management 2017]] | + | |
- | * [[MAINS.SANTANNA.DM4CRM.2018]] | + | |
- | * [[MAINS.SANTANNA.DM4CRM.2019]] | + | |
- | * [[SDM2018 | Instructions for camera ready and copyright transfer]] | + | |
- | * [[DM-SAM | Storie dell' | + |