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magistraleinformatica:dmi:start [12/11/2021 alle 00:49 (2 mesi fa)]
Anna Monreale [First Semester]
magistraleinformatica:dmi:start [14/01/2022 alle 01:37 (11 giorni fa)] (versione attuale)
Anna Monreale [Exams]
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 |17.| 27.10  14:15-16:00 | Neural Networks  |  {{ :magistraleinformatica:dmi:15_neural_networks_2021.pdf |}}| Chap. 4 Kumar Book|  | |17.| 27.10  14:15-16:00 | Neural Networks  |  {{ :magistraleinformatica:dmi:15_neural_networks_2021.pdf |}}| Chap. 4 Kumar Book|  |
 |18.| 28.10  14:15-16:00 | Python Lab on Classification | {{ :magistraleinformatica:dmi:adult_classification_2021.ipynb.zip |}} | |  | |18.| 28.10  14:15-16:00 | Python Lab on Classification | {{ :magistraleinformatica:dmi:adult_classification_2021.ipynb.zip |}} | |  |
-|19.| 29.11  09:00-10:45 | Canceled |  |   | |  + | 29.11  09:00-10:45 | Canceled |  |   | |  
-|20.| 03.11  14:15-16:00 | Python Lab on Classification + Association Rule Mining  | {{ :magistraleinformatica:dmi:classificationpython2.zip |}} {{ :magistraleinformatica:dmi:17_association_analysis2021.pdf |}} | Chap.5 Association Rules: Kumar Book|  | +|19.| 03.11  14:15-16:00 | Python Lab on Classification + Association Rule Mining  | {{ :magistraleinformatica:dmi:classificationpython2.zip |}} {{ :magistraleinformatica:dmi:17_association_analysis2021.pdf |}} | Chap.5 Association Rules: Kumar Book|  | 
-|21.| 04.11  14:15-16:00 | Association Rule Mining |   | |  Chap.5 Association Rules: Kumar Book| +|20.| 04.11  14:15-16:00 | Association Rule Mining |   | |  Chap.5 Association Rules: Kumar Book| 
-|22.| 05.11  09:00-10:45 | FP-Growth - Sequential Pattern Mining | {{ :magistraleinformatica:dmi:17_2021-fp-growth.pdf |}} |   | Chap.6 Kumar Book|  +|21.| 05.11  09:00-10:45 | FP-Growth - Sequential Pattern Mining | {{ :magistraleinformatica:dmi:17_2021-fp-growth.pdf |}} |   | Chap.6 Kumar Book|  
-|23.| 10.11  14:15-16:00 | Sequential Pattern Mining |  {{ :magistraleinformatica:dmi:18_sequential_patterns_2021.pdf |}} |Chap.7 Kumar Book |  | +|22.| 10.11  14:15-16:00 | Sequential Pattern Mining |  {{ :magistraleinformatica:dmi:18_sequential_patterns_2021.pdf |}} |Chap.7 Kumar Book |  | 
-|24.| 11.11  14:15-16:00 | Time Series Similarities, Transformations & Clustering | {{ :magistraleinformatica:dmi:22_time_series_similarity_2021.pdf |}}  | [[https://cs.gmu.edu/~jessica/BookChapterTSMining.pdf|Overview on DM for time series]]| +|23.| 11.11  14:15-16:00 | Time Series Similarities, Transformations & Clustering | {{ :magistraleinformatica:dmi:22_time_series_similarity_2021.pdf |}}  | [[https://cs.gmu.edu/~jessica/BookChapterTSMining.pdf|Overview on DM for time series]]| 
-|25.| 12.11  09:00-10:45 | Motif & Shapelet Discovery | {{ :magistraleinformatica:dmi:23_time_series_motif-2021.pdf |}} | {{ :magistraleinformatica:dmi:matrixprofile.pdf |}}  {{ :magistraleinformatica:dmi:shaplet.pdf |}} | |+|24.| 12.11  09:00-10:45 | Motif & Shapelet Discovery | {{ :magistraleinformatica:dmi:23_time_series_shapelets-motif-2021.pdf |}} | {{ :magistraleinformatica:dmi:matrixprofile.pdf |}}  {{ :magistraleinformatica:dmi:shaplet.pdf |}} | 
 +|25.| 17.11  14:15-16:00 | Lab: Association Rules & Sequential pattern mining by Python |  {{ :magistraleinformatica:dmi:arm-spm.zip |}} |  |  | 
 +|26.| 18.11  14:15-16:00 | Ethics & Privacy | {{ :magistraleinformatica:dmi:19_ethics_privacy2021.pdf |}} > | {{ :dm:allegato1_chapter.pdf | Overview on Privacy}}  {{ :magistraleinformatica:dmi:allegato11-cpdp13.pdf |}}{{ :dm:capprivacy.pdf | Privacy by design}} 
 +|27.| 19.11  09:00-10:45 | Lab: Time series |   {{ :magistraleinformatica:dmi:timeseries-py.zip |}} | | 
 +|28.| 24.11  14:15-16:00 | Explainability | {{ :magistraleinformatica:dmi:20_explainability_2021.pdf |}}  | Material: [[https://arxiv.org/pdf/1805.10820.pdf|LORE]] [[https://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf| LIME]]   [[http://delivery.acm.org/10.1145/3240000/3236009/a93-guidotti.pdf?ip=94.38.73.6&id=3236009&acc=OA&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2ED544636226B69D47&__acm__=1576196869_06b3353aae4fe3bd8ea30d9c9c5356eb|Survey]] {{ :magistraleinformatica:dmi:pkdd_2019_abele_cr.pdf |ABELE}} [[https://arxiv.org/pdf/1705.07874.pdf|SHAP]] {{ :magistraleinformatica:dmi:lasts.pdf | LASTS}}  | | 
 +|29.| 25.11  14:15-16:00 | Explainability + LAB XAI| {{ :magistraleinformatica:dmi:xai-lab.zip |}}  |  | | 
 +|30.| 26.11  09:00-10:45 | LAB XAI + Anomaly Detection  |  {{ :magistraleinformatica:dmi:21_anomaly_detection_2020.pdf | AD&OD}} |  |  | 
 +|31.| 01.12  14:15-16:00 | Anomaly Detection + Lab |  {{ :magistraleinformatica:dmi:anomalydetection-1.ipynb.zip |ADPY}} |  |  | 
 +|32.| 02.12  14:15-16:00 | CRISP-DM | {{ :magistraleinformatica:dmi:crisp-dm.pdf |}}  |  | | 
 +|.  | 03.12  09:00-10:45  |  Canceled |  |  | 
 +|33.| 15.12  14:15-16:00 Room C| Paper Presentation |    |  | 
 +|34.| 16.12  14:15-16:00 Room C| Paper Presentation |    | | 
 +|35.| 17.12  09:00-10:45 Room C| Paper Presentation |    |  |
  
 ====== Exams ====== ====== Exams ======
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 A project consists in data analyses based on the use of data mining tools.  A project consists in data analyses based on the use of data mining tools. 
-The project has to be performed by a team of 2/3 students. It has to be performed by using Python. The guidelines require to address specific tasks. Results must be reported in a unique paper. The total length of this paper must be max 20 pages of text including figures. The students must deliver both: paper (single column) and  well commented Python Notebooks.+The project has to be performed by a team of 2/3 students. It has to be performed by using Python. The guidelines require to address specific tasks. Results must be reported in a unique paper. The total length of this paper must be max 25 pages of text including figures. The students must deliver both: paper (single column) and  well commented Python Notebooks.
  
   * First part of the project consists in the **assignments** described here: {{ :magistraleinformatica:dmi:data_mining_project_2021_1_.pdf | Project Description}}   * First part of the project consists in the **assignments** described here: {{ :magistraleinformatica:dmi:data_mining_project_2021_1_.pdf | Project Description}}
-     **Dataset:** {{ :magistraleinformatica:dmi:prj_data.zip | Dataset}} +  - **Dataset:** {{ :magistraleinformatica:dmi:prj_data.zip | Dataset}} 
-     * **Deadline**: the fist part has to be delivered within  November, <del>5th 2021</del> 15th 2021.Send an email to: anna.monreale@unipi.it and francesca.naretto@sns.it +  **Deadline**: the fist part has to be delivered within  November, <del>5th 2021</del> 15th 2021.Send an email to: anna.monreale@unipi.it and francesca.naretto@sns.it 
    
 +  * Second part of the project consists in the assignment Task 3 described here: {{ :magistraleinformatica:dmi:data_mining_project_2021-2.pdf |Updated Project Description}}
 +     - **Deadline**: 5th January 2022
 +
 +  * Third part of the project consists in the assignment Task 4 described here: {{ :magistraleinformatica:dmi:data_mining_project_2021-6.pdf  |Updated Project Description}}
 +   - Note that the document contains also rules for the delivery and final exam!
 +   - Data for time series analysis: {{ :magistraleinformatica:dmi:cityglobaltemperature2000-2009.csv.zip |CityTemp}}
 +   - **Deadline**: 5th January 2022
 +
 +
 +**Students who did not deliver the above project within 5th Jan 2022 need to ask by email a new project to the teachers. The project that will be assigned will require about 2 weeks of work and after the delivery it will be discussed during the oral exam. **
 +
 ** Paper Presentation (OPTIONAL)** ** Paper Presentation (OPTIONAL)**
  
-Students need to present a research paper (made available by the teacher) during the last week of the course. This presentation is OPTIONAL: Students that decide to do the paper presentation can avoid the oral exam with open questions. They only need to present the project (see next point).+Students need to present a research paper (made available by the teacher) during the last week of the course. This presentation is OPTIONAL: Students that decide to do the paper presentation can avoid the oral exam with open questions. They only need to present the project (see next point). The paper presentation can be done by the group or by a single person.
  
 **Oral Exam** **Oral Exam**
magistraleinformatica/dmi/start.1636678195.txt.gz · Ultima modifica: 12/11/2021 alle 00:49 (2 mesi fa) da Anna Monreale