| Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente |
| magistraleinformatica:dmi:start [11/12/2025 alle 11:44 (5 giorni fa)] – [Exam] Anna Monreale | magistraleinformatica:dmi:start [14/12/2025 alle 23:58 (33 ore fa)] (versione attuale) – [Past Excercises and past exams of similar courses] Anna Monreale |
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| ===== Past Excercises and past exams of similar courses ===== | ===== Past Excercises and past exams of similar courses ===== |
| * Exercises on Clustering: {{ :dm:ex._clustering.pdf |}} | * Exercises on Clustering: {{ :dm:ex._clustering.pdf |}} |
| * Some text of past exams of a similar course: {{ :dm:2017-1-19.pdf |}}, {{ :dm:2017-9-6.pdf |}}, {{ :dm:2016-05-30-dm1-seconda.pdf |}} | * Some text of past exams of a similar course: {{ :dm:2017-1-19.pdf |}}, {{ :dm:2017-9-6.pdf |}}, {{ :dm:2016-05-30-dm1-seconda.pdf |}}, {{ :dm:dm2_exam.2017.06.13_solutions.pdf |}}, {{ :dm:dm2_exam.2017.07.04_solutions.pdf |}}, {{ :dm:dm2_mid-term_exam.2017.06.06_solutions.pdf |}} |
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| * Some solutions of past exams containing exercises on KNN and Naive Bayes classifiers: {{ :dm:dm2_exam.2017.06.13_solutions.pdf |}}, {{ :dm:dm2_exam.2017.07.04_solutions.pdf |}}, {{ :dm:dm2_mid-term_exam.2017.06.06_solutions.pdf |}} | |
| * Some exercises (partially with solutions) on **sequential patterns** and **time series** can be found in the following texts of exams from the last years: {{ :dm:dm2_exam.2015.04.13.results.pdf|}}, {{ :dm:dm2_exam.2016.04.4_sol.pdf |}}, {{ :dm:dm2_exam.2016.04.5_sol.pdf |}}, {{ :dm:dm2_exam.2016.06.20_sol.pdf |}}, {{ :dm:dm2_exam.2016.07.08_sol.pdf |}} | * Some exercises (partially with solutions) on **sequential patterns** and **time series** can be found in the following texts of exams from the last years: {{ :dm:dm2_exam.2015.04.13.results.pdf|}}, {{ :dm:dm2_exam.2016.04.4_sol.pdf |}}, {{ :dm:dm2_exam.2016.04.5_sol.pdf |}}, {{ :dm:dm2_exam.2016.06.20_sol.pdf |}}, {{ :dm:dm2_exam.2016.07.08_sol.pdf |}} |
| * 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: {{tdm:verifica2006.pdf|Verifica 2006}}, {{tdm:verifica2005.pdf|Verifica 2005 (con soluzioni)}}, {{tdm:verifica2004.pdf|Verifica 2004}}, {{dm:verifica.05.06.2007.pdf|Verifica 5 giugno 2007}}, {{dm:verifica.26.06.2007.pdf|Verifica 26 giugno 2007}}, {{dm:verifica.24.07.2007_corretto.pdf|Verifica 24 luglio 2007}} (e {{:dm:soluzioni.2008.04.03.pdf|Soluzioni}}), {{:dm:dm-tdm.appello_2008_07_18_parte1.pdf|Verifica 18 luglio 2008 - parte 1}}, {{:dm:dm-tdm.appello_2008_07_18_parte2.pdf|Verifica 18 luglio 2008 - parte 2}},{{: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}} | * 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: {{tdm:verifica2006.pdf|Verifica 2006}}, {{tdm:verifica2005.pdf|Verifica 2005 (con soluzioni)}}, {{tdm:verifica2004.pdf|Verifica 2004}}, {{dm:verifica.05.06.2007.pdf|Verifica 5 giugno 2007}}, {{dm:verifica.26.06.2007.pdf|Verifica 26 giugno 2007}}, {{dm:verifica.24.07.2007_corretto.pdf|Verifica 24 luglio 2007}} (e {{:dm:soluzioni.2008.04.03.pdf|Soluzioni}}), {{:dm:dm-tdm.appello_2008_07_18_parte1.pdf|Verifica 18 luglio 2008 - parte 1}}, {{:dm:dm-tdm.appello_2008_07_18_parte2.pdf|Verifica 18 luglio 2008 - parte 2}},{{: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}} |
| |27. | 27.11 | Time series, Python Supervised Learning & Imbalanced Scenarios | {{ https://github.com/data-mining-UniPI/teaching25/blob/lectures/time%20series/Time%20series.html.pdf | Slides }}, {{ https://github.com/data-mining-UniPI/teaching25/blob/lectures/time%20series/Time%20series.html | Slides in HTML (w/ working animation) }} | | Setzu | | |27. | 27.11 | Time series, Python Supervised Learning & Imbalanced Scenarios | {{ https://github.com/data-mining-UniPI/teaching25/blob/lectures/time%20series/Time%20series.html.pdf | Slides }}, {{ https://github.com/data-mining-UniPI/teaching25/blob/lectures/time%20series/Time%20series.html | Slides in HTML (w/ working animation) }} | | Setzu | |
| |28. | 02.12 | Shapelet-based Classification, Motif discovery | {{ :magistraleinformatica:dmi:23_time_series_motif-shapelets2023.pdf |Slides}} | {{ :magistraleinformatica:dmi:shaplet.pdf |}} {{ :magistraleinformatica:dmi:matrixprofile.pdf |}} [[https://www.cs.ucr.edu/~eamonn/MatrixProfile.html|Papers and resourse on motif]] |Monreale | | |28. | 02.12 | Shapelet-based Classification, Motif discovery | {{ :magistraleinformatica:dmi:23_time_series_motif-shapelets2023.pdf |Slides}} | {{ :magistraleinformatica:dmi:shaplet.pdf |}} {{ :magistraleinformatica:dmi:matrixprofile.pdf |}} [[https://www.cs.ucr.edu/~eamonn/MatrixProfile.html|Papers and resourse on motif]] |Monreale | |
| |29. | 03.12 | Py: Time Series|{{ :magistraleinformatica:dmi:timeseries.zip |}}||Monreale, Mannocci | | |29. | 03.12 | Py: Time Series|{{ :magistraleinformatica:dmi:timeseries.zip |}}| | Monreale, Mannocci | |
| |30. | 04.12 | | |30. | 04.12 | Responsible AI: introduction and EU Regulations | {{ :magistraleinformatica:dmi:19_rai_privacy2025.pdf | Slides}}|Monreale | |
| |31. | 09.12 | | |31. | 09.12 | Responsible AI: privacy. | Same slides of previous lecture | {{ :magistraleinformatica:dmi:chap-anonymity.pdf |}} [[https://arxiv.org/abs/1610.05820|MIA attack against ML]]| Monreale| |
| |32. | 10.12 | | |32. | 10.12 | Responsible AI: Explaianble AI |{{ :magistraleinformatica:dmi:20_explainability_2025.pdf |XAI}}|[[https://christophm.github.io/interpretable-ml-book/|Digital book where students can find some basic XAI models and notions]] {{ :magistraleinformatica:dmi:xai-taxonomy-survey.pdf | XAI Survey describing the taxonony and dimensions of XAI}} {{ :magistraleinformatica:dmi:lore-j.pdf | LORE apaproach}}, {{ :magistraleinformatica:dmi:abele-approach.pdf |ABELE approach}}{{ :magistraleinformatica:dmi:lasts_-_explaining_any_time_series_classifier_2_.pdf |LASTS}} [[https://arxiv.org/abs/1705.07874|SHAP]][[https://arxiv.org/abs/1602.04938|LIME]]|Monreale| |
| |33. | 11.12 | | |33. | 11.12 | XAI Python Notebook + Private and explanable FL, Assessing privacy in XAI | {{ :magistraleinformatica:dmi:11-dic-2025-xai.pdf | Slides}} |{{ :magistraleinformatica:dmi:glor-flex_local_to_global_rule-based_explanations_fl.pdf |GLOR-FLEX}} {{ :magistraleinformatica:dmi:fastshap-ex-pri.pdf |FASTSHAP++}} [[https://www.tdp.cat/issues21/tdp.a534a24.pdf|REVEAL]]|Naretto| |
| |34. | 16.12 |Project Presentations - second check - **MANDATORY **| | |34. | 16.12 |Project Presentations - second check - ONLINE - **MANDATORY **| |
| |35. | 17.12 |Project Presentations - second check - **MANDATORY **| | |35. | 17.12 |Project Presentations - second check - ONLINE - **MANDATORY **| |
| |36. | 18.12 |Project Presentations - second check - **MANDATORY **| | |36. | 18.12 |Project Presentations - second check - ONLINE - **MANDATORY **| |
| ====== Exam ====== | ====== Exam ====== |
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| * **Dataset Task 4.** https://drive.google.com/file/d/1Li2roWMoREN6_nKy-trB7pXWDA1xkAzh/view?usp=sharing | * **Dataset Task 4.** https://drive.google.com/file/d/1Li2roWMoREN6_nKy-trB7pXWDA1xkAzh/view?usp=sharing |
| * ** Project description Task 5. | * ** Project description Task 5. |
| * **Project Question & Answers.** {{ :magistraleinformatica:dmi:25-26-data_mining_project_includingt5.pdf |Complete Project Description}} | * **Project Question & Answers.{{ :magistraleinformatica:dmi:25-26-data_mining_project_includingt5.pdf |Complete Project Description}} |
| * **Deadline.** January 5th, 2026. | * **Deadline.** January 5th, 2026. |
| * **Delivery instructions.** The final deadline of the project is **5th January 2026 at 23:59**. This deadline is **STRICT**. No extension is possible because then the winter session of exams starts. **Groups that will not deliver the project by 5th January will need to do the written exam during the exam sessions.** Each group must deliver by email to anna.monreale@unipi.it, mattia.setzu@unipi.it, lorenzo.mannocci@di.unipi.it a zipped folder named DM_GroupID.zip and containing 4 folders and 1 pdf file: a folder named DM_GroupID_TASK1, containing source code of data understanding; a folder named DM_GroupID_TASK2, containing source code of data clustering; a folder named DM_GroupID_TASK3, containing source code of classification and explanation analysis; a folder named DM_GroupID_TASK4, containing source code of time series analysis; a pdf file with maximum 25+2 pages including figures discussing the results of the tasks (25 pages for tasks 1-4 and 2 pages for task 5). The name of this file must be: DM_Report_GroupID.pdf. The file must contain the list of authors (i.e., members of the group). **The subject of the email must be “DMProject25_GroupID”** | * **Delivery instructions.** The final deadline of the project is **5th January 2026 at 23:59**. This deadline is **STRICT**. No extension is possible because then the winter session of exams starts. **Groups that will not deliver the project by 5th January will need to do the written exam during the exam sessions.** Each group must deliver by email to anna.monreale@unipi.it, mattia.setzu@unipi.it, lorenzo.mannocci@di.unipi.it a zipped folder named DM_GroupID.zip and containing 4 folders and 1 pdf file: a folder named DM_GroupID_TASK1, containing source code of data understanding; a folder named DM_GroupID_TASK2, containing source code of data clustering; a folder named DM_GroupID_TASK3, containing source code of classification and explanation analysis; a folder named DM_GroupID_TASK4, containing source code of time series analysis; a pdf file with maximum 25+2 pages including figures discussing the results of the tasks (25 pages for tasks 1-4 and 2 pages for task 5). The name of this file must be: DM_Report_GroupID.pdf. The file must contain the list of authors (i.e., members of the group). **The subject of the email must be “DMProject25_GroupID”** |
