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magistraleinformatica:dmi:start [09/12/2022 alle 14:13 (16 mesi fa)]
Anna Monreale [First Semester]
magistraleinformatica:dmi:start [22/03/2024 alle 20:34 (6 giorni fa)] (versione attuale)
Anna Monreale [First Semester]
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-====== Data Mining (309AA) - 9 CFU A.Y. 2022/2023 ======+====== Data Mining (309AA) - 9 CFU A.Y. 2023/2024 ======
  
 **Instructor:** **Instructor:**
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     * [[anna.monreale@unipi.it]]        * [[anna.monreale@unipi.it]]   
 **Teaching Assistant:** **Teaching Assistant:**
-  * **Francesca Naretto** 
-    * KDDLab, SNS, Pisa 
-    * [[francesca.naretto@sns.it]]   
   * * **Lorenzo Mannocci**   * * **Lorenzo Mannocci**
     * University of Pisa     * University of Pisa
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 ====== News ====== ====== News ======
-  * [28.10.2022] ** The lectures on 16 and 17 November will be suppressed. **  +  * [05.09.2023] ** The lectures will start on 27th September 2023** 
-  * [09.09.2022] he lectures will be only in presence and will NOT be live-streamed, but recordings of the lecture or of the previous years will be made available here for non-attending students.+
    
 ====== Learning Goals ====== ====== Learning Goals ======
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 ^  Day of Week  ^  Hour  ^  Room  ^  ^  Day of Week  ^  Hour  ^  Room  ^ 
-|  Wednesday |  09:00 - 11:00  |  Room  |  +|  Wednesday |  09:00 - 11:00  |  Room C1  |  
-|  Thursday  |  11:00 - 13:00  |  Room  +|  Thursday  |  09:00 - 11:00  |  Room C1  
 |  Friday    |  09:00 - 11:00  |  Room C  |  |  Friday    |  09:00 - 11:00  |  Room C  | 
  
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 **Office hours - Ricevimento:** **Office hours - Ricevimento:**
 Anna Monreale: Tuesday: 11:00-13:00 by online using Teams or at the Department of Computer Science, room 374/E (Please ask an appointment by email). Anna Monreale: Tuesday: 11:00-13:00 by online using Teams or at the Department of Computer Science, room 374/E (Please ask an appointment by email).
-Francesca Naretto: TDB+Lorenzo Mannocci: TDB
  
-**[[https://teams.microsoft.com/l/team/19%3aU9V_a8O2AkYl6KAcYiVMyOx_UfVD4SXKE2bwYRdOQ581%40thread.tacv2/conversations?groupId=ecc43c6e-29fe-4819-bc96-9bc6b906491f&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Teams Channel]]** will be used ONLY to post news, Q&A, and other stuff related to the course. The lectures will be only in presence and will **NOT** be live-streamed, but recordings of the lecture or of the previous years will be made available here for non-attending students.  +A [[https://teams.microsoft.com/l/team/19%3ajujTZ5yI6IyKkRl1YEGY0Iisg7RhlW1YTam_NO3-OOE1%40thread.tacv2/conversations?groupId=2ce9fd1a-3f23-47b0-92cd-8652f8be9ed6&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Teams Channel]] will be used ONLY to post news, Q&A, and other stuff related to the course. The lectures will be only in presence and will **NOT** be live-streamed, but recordings of the lecture or of the previous years will be made available here for non-attending students.  
 ====== Learning Material -- Materiale didattico ====== ====== Learning Material -- Materiale didattico ======
  
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     * [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php]]     * [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php]]
     * Chapters 4,6 and 8 are also available at the publisher's Web site.     * Chapters 4,6 and 8 are also available at the publisher's Web site.
-  * 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.
   *  Jake VanderPlas. **[[http://shop.oreilly.com/product/0636920034919.do| Python Data Science Handbook: Essential Tools for Working with Data.]]** 1st Edition.    *  Jake VanderPlas. **[[http://shop.oreilly.com/product/0636920034919.do| Python Data Science Handbook: Essential Tools for Working with Data.]]** 1st Edition. 
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-====== Class Calendar (2022/2023) ======+====== Class Calendar (2023/2024) ======
  
 ===== First Semester  ===== ===== First Semester  =====
  
 ^ ^ Day ^ Topic ^ Learning material ^ References ^ Video Lectures ^ ^ ^ Day ^ Topic ^ Learning material ^ References ^ Video Lectures ^
-|1.  |  15.09  11:00‑13:00 | Overview. Introduction to KDD   |{{ :magistraleinformatica:dmi:1-overview.pdf |}} {{ :magistraleinformatica:dmi:1-intro-dm.pdf |}}|Chap. 1 Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EShM9RVJWTNHq_V8gj-abVgB86BMP0QX-iNMLzrrNP_sPg?e=6uigHe|Video 1: Course Overview]];[[https://unipiit.sharepoint.com/:v:/s/a__td_54794/ESccksnIm5lFp9ROw18otfsBBhl3Ybeus-OjqqMcl2xCmQ?e=kF1KNe|Video 2: Introduction DM]] (the recording of the Introduction had some audio issue so I published the part of the lecture of the a.y. 2021/22)+|1.  |  27.09  | Overview. Introduction to KDD   | {{ :magistraleinformatica:dmi:1-overview-2023.pdf |}} {{ :magistraleinformatica:dmi:1-intro-dm.pdf |}}|Chap. 1 Kumar Book |[[https://unipiit.sharepoint.com/:v:/s/a__td_59044/EYjxO1YANqtMnr8upJa3X4oBp3wEsdjef8iSXN7LL7jcxQ?e=Jd80j9|Introduction DM - Video1]] [[ https://unipiit.sharepoint.com/:v:/s/a__td_59044/Eesf2mgGU1hMjMH4qH_xJewBKtee3TWrullu269byR2bnA?e=JJ4AUx|Introduction DM - Video2]]| 
-|2.  |  16.09  09:00-11:00 | Data Understanding | {{ :magistraleinformatica:dmi:2-data_understanding.pdf |}} |Chap.2 Kumar Book and additioanl resource of Kumar Book:[[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Exploring Data]] If you have the first ed. of KUMAR this is the Chap 3 | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EXYTYX_JE4dDsqGwmBTKyBcBCkd7IxaFhUTn-MRc7LyJlA?e=gfCgnI|Video 1: Data Understanding - Part 1]]; [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EeMWAfDY__BKvQ2RHadlmZsBgKCXuAV2mxjUiY3GuWK9Zg?e=7wIafq |Video 2: Data Understanding - Part 2]] +|2.  |  28.09  | Data Understanding | {{ :magistraleinformatica:dmi:2-data_understanding.pdf |}}|Chap.2 Kumar Book and additioanl resource of Kumar Book:[[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Exploring Data]] If you have the first ed. of KUMAR this is the Chap 3 |  
-|3.  |  21.09  09:00-11:00 | Data Understanding & Data Preparation |  {{ :magistraleinformatica:dmi:3-data_preparation.pdf |}} |Chap.2 Kumar Book and additioanl resource of Kumar Book:[[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Exploring Data]] If you have the first ed. of KUMAR this is the Chap 3 | [[ https://unipiit.sharepoint.com/:v:/s/a__td_54794/EaqvlZGIKvdMi8j8r7TIbHkB76b2K8gMsEPVtDouO5waYw?e=IUYEb6 +|3.  |  29.09  | Data Understanding & Data Preparation |  {{ :magistraleinformatica:dmi:3-data_preparation.pdf |}} |Chap.2 Kumar Book and additioanl resource of Kumar Book:[[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Exploring Data]] If you have the first ed. of KUMAR this is the Chap 3 | | 
-|Video: Data Understanding & Data Preparation]] +|4.  |  04.10  | Data Preparation Data Similarities |  {{ :magistraleinformatica:dmi:4-data_similarity.pdf |}} | Data Similarity is in Chap. 2  | [[https://unipiit.sharepoint.com/:v:/s/a__td_59044/EWaYURxnzPdIiLiqjkS4LM8B8sme_xmm0LwtK9EptuP0Jg?e=dsZojO|DP+Similarities]] The last minutes of the lecture were not recorded because of the connection| 
-|4.  |  22.09   11:00-13:00 | Data Preparation Data Similarities.|{{ :magistraleinformatica:dmi:4-data_similarity.pdf |}}       | Data Similarity is in Chap. 2  |[[ https://unipiit.sharepoint.com/:v:/s/a__td_54794/EarSFVCS5MFJnFSi9dMT2y0BJgqS_YIVLX9fenQV9GyrjQ?e=eZshby +|5.  |  05.10  | Python-LAB: Data Understanding | {{ :magistraleinformatica:dmi:dataunderstanding.zip | DU notebooks and data}} |  | [[https://unipiit.sharepoint.com/:v:/s/a__td_59044/EYWSZBIG7X1MoFOev5Th_cIBprLLN-AwSBMamgGzNju0Sw?e=jzdPx8|Python Lab on DU]]| 
-|Video 1: Data Preparation Data Similarities - Part 1]][[ https://unipiit.sharepoint.com/:v:/s/a__td_54794/EQMiVHPB8hlKuZ7ntw8Km-IBe4HtW_hz5VvYefLvHDfDLQ?e=bKJOn7|Video 2: Data Preparation + Data Similarities - Part 2]]   +  06.10  | Suppressed |  |  | | 
-|5.  |  23.09  09:00-11:00 | Introduction to Clustering. Center-based clusteringkmeans| {{ :magistraleinformatica:dmi:5-basic_cluster_analysis-intro.pdf |}}  {{ :magistraleinformatica:dmi:6.1-basic_cluster_analysis-kmeans.pdf |}} | Clustering is in Chap. 7  |[[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EV-fDd75MIxGmazA79kFHCYBI78yYwqy7AFE5h9MN2rRqg?e=YVgdjS|Video 1: Introduction to Clustering + K-means - Part 1]];[[https://unipiit.sharepoint.com/:v:/s/a__td_54794/ETySd1UWIzxCoAKilzaXO_MBW8oXZZCjf5FEhyywGIdJBg?e=Xq2jdo|Video 2: Introduction to Clustering + K-means - Part 2]]] +|6.  |  11.10  | Introduction to Clustering. Centroid-based ClusteringK-means algorithm. | {{ :magistraleinformatica:dmi:5-basic_cluster_analysis-intro.pdf |}} {{ :magistraleinformatica:dmi:6.1-basic_cluster_analysis-kmeans.pdf |}} | Chap. 7 Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EV-fDd75MIxGmazA79kFHCYBI78yYwqy7AFE5h9MN2rRqg?e=YVgdjS|Video 1: Introduction to Clustering + K-means - Part 1]] Video of previous years
-|6.  |  28.09  09:00-11:00 | Python Lab: Data Understanding & Data Preparation |{{ :undefined:dataund.zip Notebook DU tips}} | | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/Eb0GU3ScaudIuh3kmNVw5_EBvgFRME5hnkOyZCetW55vwg?e=fDgxnE|Video 1Python LabDU - Part1]];[[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EfTSxKrV45lIhWCySWDflM4BXjD7WSKj6X3Se5Dv7UEb2Q?e=vf2E2h|Video 2: Python Lab: DU Part2]]+|7.  |  12.10  | Centroid-based ClusteringK-means variants. | {{ :magistraleinformatica:dmi:6.2-basic_cluster_analysis-kmeans-variants.pdf |}} | Chap7 Kumar Book {{ :magistraleinformatica:dmi:clusteringmixturemodels.pdf |}} {{ :magistraleinformatica:dmi:xmeans.pdf |}}| [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/ETySd1UWIzxCoAKilzaXO_MBW8oXZZCjf5FEhyywGIdJBg?e=Xq2jdo|Video 2: Introduction to Clustering + K-means - Part 2]]]  [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EQTbbvqF2kJOgEsFQ1WF48cBjWf2wgTCbOjxcQzn9MyVzw?e=KQ7gEZ|Video 1Center-based clustering - Bisecting K-means, Xmeans, EM ]];Videos of previous years
-|7.  |  29.09  11:00-13:00 | Hierarchical clustering | {{ :magistraleinformatica:dmi:7.basic_cluster_analysis-hierarchical.pdf |}}| | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EcBupaEx_HNFiEMYa_dQ2m8BNe25abzcmTKZ3JrVOtibCQ?e=SSgN9T| Video: Project Description + Hierarchical Clustering]]| +|  |  13.10  Suspension of teaching   Recording in Teams Channel 
-|  |  30.09  09:00-11:00 Lecture Canceled | | | | +|8.|  18.10  | Hierarchical and density based CLustering | {{ :magistraleinformatica:dmi:7.basic_cluster_analysis-hierarchical.pdf |}} {{ :magistraleinformatica:dmi:8.basic_cluster_analysis-dbscan-validity.pdf |}} |  Chap. 7 Kumar Book | Recording in Teams Channel  
-|8.  |  05.10  09:00-11:00 Density based clusteringClustering validity. | {{ :magistraleinformatica:dmi:8.basic_cluster_analysis-dbscan-validity.pdf |}} | Chap. 7 Kumar Book    +|9.|  19.10  | Clustering Validity & Python LabClusterig K-means | {{ :magistraleinformatica:dmi:8.basic_cluster_analysis-dbscan-validity.pdf |}} |  Chap. 7 Kumar Book| Recording in Teams Channel  
-|9.|   06.10  11:00-13:00 Center-based clusteringBisecting K-means, Xmeans, EM       | {{ :magistraleinformatica:dmi:6.2-basic_cluster_analysis-kmeans-variants.pdf |}} | Chap. 7 Kumar Book, {{ :magistraleinformatica:dmi:clusteringmixturemodels.pdf |}} {{ :magistraleinformatica:dmi:xmeans.pdf |}}| [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EQTbbvqF2kJOgEsFQ1WF48cBjWf2wgTCbOjxcQzn9MyVzw?e=KQ7gEZ|Video 1: Center-based clustering - Bisecting K-means, Xmeans, EM ]]; [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EYc-39UkUhlCsL_huteYf7YBJjTruY207hGwBJmBRobACg?e=ixfbJ2|Video 2: Clustering Lab.]] +|10.|  20.10 | Python Lab: Clusterig Density based and hierarchical + Introduction to Classification |{{ :magistraleinformatica:dmi:clustering.zip | Notebook on Clustering}} {{ :magistraleinformatica:dmi:9.chap3_basic_classification-2023.pdf |}} | Chap.3 Kumar Book |Recording in Teams Channel 
-|10.|   07.10  09:00-11:00 |Python Lab - Clustering {{ :magistraleinformatica:dmi:tips_clustering.ipynb_complete.zip | Notebook CLustering Tips}}   |  |[[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EUl3UMFUoixPo9FzX7nC9e4BWS1SFpIGPVdZbwyfdZBgCw?e=BexKor|Video: Clustering Lab. - Part2]] | +|11.|  25.10 | Decision Trees & Classifier Evaluation | Same slides as previous lecture | Chap.3 Kumar Book | Recording in Teams Channel     
-|11.|   12.10  09:00-11:00 |Classification Problem. Decision Trees|  {{ :magistraleinformatica:dmi:9.chap3_basic_classification-2022.pdf |}}  | Chap. 3 Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EWVzwCQWC7BKmRlG69Regg4BEmeqRwin9GZ0VJIcV_wtsw?e=YDkrrf| Video Lecture - Part1]]; [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EQUgcfNC2XdPn6QHy3rlgJcBO9UTsgmoMYFJbhP8vM5UIA?e=uWDpVA|Video Lecture - Part 2]]+|12.|  26.10 | Classifier Evaluation | Same slides as previous lecture | Chap.3 Kumar Book |     
-|12.|   13.10  11:00-13:00 |Decision Trees & Classifier Evaluation|  same slides previous lecture | Chap. 3 Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EaJcTBiLgh1DiErGVCZAovoBlLOaHuCrabxtNOTqXYRg-A?e=IQJDGE|Video Lecture - Part 1]] [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EbXLwpQCdNNMt4ClRGPAivMBFJa-qoH8N9TKpp9OgD8mlw?e=TjOAsx|Video lecture - Part 2]]+|13.|  27.10 | Rule-based Classifiers |{{ :magistraleinformatica:dmi:10-rule-based-classifiers.pdf |}} | Chap.4 Kumar Book |  Recording in Teams Channel    
-|13.|   14.10  09:00-11:00 |Classifier Evaluation|  same slides previous lecture | Chap. 3 Kumar Book |[[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EXT98sRBqL9Dpf-pAzfUnkIBy2zDxh86kI2D8ouBXH1zxQ?e=gw3zzM|Video Lecture]] +|14.|  02.11 | Rule-based Classifiers + Instance based Classifiers| {{ :magistraleinformatica:dmi:10-knn.pdf |}}| Chap.4 Kumar Book Recording in Teams Channel     
-|14.|   19.10  09:00-11:00 |Rule based Classifiers |  {{ :magistraleinformatica:dmi:10-rule-based-clussifiers-2022.pdf |}}{{ :magistraleinformatica:dmi:10-knn-2022.pdf |}} | Rule based classifiers: Chap. 5.1, KNN: Chap. 4.2 - Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EYWGSnZlI1BMr8CO6QZDMJEBdKQhL5_GAx0YqMAgR-49Fg?e=JSvQPJ|Video 1: Rule based classifiers]][[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EYWGSnZlI1BMr8CO6QZDMJEBdKQhL5_GAx0YqMAgR-49Fg?e=1qMLMf|Video 2: KNN]] +|15.|  03.11 |Naive Bayesian Classifier. SVM. Ensemble Classifiers| {{ :magistraleinformatica:dmi:11_2023-naive_bayes.pdf |}} {{ :magistraleinformatica:dmi:14_svm_2023.pdf |}} {{ :magistraleinformatica:dmi:13_ensemble_2023.pdf |}}| Chap.4 Kumar Book | Recording in Teams Channel   |   
-|15.|   20.10  11:00-13:00 |DT simulation of the learning algorithm  {{ :magistraleinformatica:dmi:2021-dt-ex.pdf | DT Exercise}}|  | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EaFgtNLiCONAnV6NF7DpZioB8W09o2HQcxJsSzvm65Pb_w?e=SvNSgj|Video 1: DT-EX]]; [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EaOBQeDA_QtLtRYQVHoDFgABilFcAgpYsm01aUdUNjvBGA?e=bFpIpz|Video 2: DT-EX]]+|16.|  08.11 | Python Lab: Classification|  {{ :magistraleinformatica:dmi:classification.zip |}} | | Recording in Teams Channel     
-|16.|   21.10  09:00-11:00 |Naive Bayesian Classifier. SVM. Ensemble Classifiers | {{ :magistraleinformatica:dmi:11_2022-naive_bayes.pdf |}} {{ :magistraleinformatica:dmi:14_svm_2022.pdf |}} {{ :magistraleinformatica:dmi:13_ensemble_2022.pdf |}}| Chap. 4 Kumar Book |[[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EX8DmHrg7d5HgaOstPoOjNcBwtfdTk9vbNoCeLQZSAWXYA?e=l3p65j|Video1]]; [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EeVLe8Gr5-lFtAwfPipFTSQB95Wo0HzeLvo2O9aAN3a8_w?e=V2EmcA|Video2]]| +|17.|  09.11 | NN Classifiers| {{ :magistraleinformatica:dmi:15_neural_networks_2023.pdf |}} | Chap.4 Kumar Book Recording in Teams Channel     
-|17.|   26.10  09:00-11:00 |Ensemble Classifiers + NN Classifiers + Project Discussion| same slides of the previous lecture  | Chap. 4 - Kumar Book  | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EZWw_m9HxzlKlIss5vsPeIgBl70TAqJW_I0j5x6rtkTOZg?e=kPTZFu|Video1]]| +|18.|  10.11 | Python Lab: NN & Imbalanced Classification | {{ :magistraleinformatica:dmi:imbalanced_classification.zip |}} |  | Recording in Teams Channel   |   
-|18.|   27.10  11:00-13:00 NN Classifiers + Python Lab: Classification| {{ :magistraleinformatica:dmi:15_neural_networks_2021.pdf |}} {{ :magistraleinformatica:dmi:adult_classification_2021.ipynb.zip Classificaton Notebook}} {{ :magistraleinformatica:dmi:adult.data.zip Adult Dataset}}  | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EVt50MbiGTpKsiO_t7WIeP0B8_ocGvnD7zEeUyRQ_d5wwQ?e=squ80v|Video1]]; [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EcN1nMk11VBKgLbOg-2KWakBStYobApkO45gI9FZg1E_2Q?e=WP2MqH|Video2]]+|19.|  15.11 | Association Rule Mining: Apriori | {{ :magistraleinformatica:dmi:17_association_analysis.pdf |}} | Chap.5 Kumar Book |  Recording in Teams Channel    
-|19.|   28.10  09:00-11:00 |Python Lab: Classification | {{ :magistraleinformatica:dmi:adult_classification_2021.ipynb.zip | Classificaton Notebook}} (same as previous lecture) |  | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EVBxay2OtPhLmVgn658oHqEBMyVzNKp2Ju5aMq3ecTrOZw?e=e2uONt|Video]] | +|20.|  16.11 | Association Rule MiningEvalaution and FP-Growth  | {{ :magistraleinformatica:dmi:17_2023-fp-growth.pdf |}} | Chap.5 Kumar Book |  Recording in Teams Channel  
-|20.|   02.11  09:00-11:00 |Python Lab: NN & Imbalanced Classification | |  | Unfortunately Video is not available for technical issues  | +|21.|  17.11 | Sequential Pattern Mining | {{ :magistraleinformatica:dmi:18_sequential_patterns_2023.pdf |}} | Chap.6 Kumar Book  Recording in Teams Channel  
-|21.|   03.11  11:00-13:00 | Association Rule Mining| {{ :magistraleinformatica:dmi:17_association_analysis2021.pdf |}} | Chap. 5 Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EWKi0iWArRhAgi8T5pKNsiMB5llNPPF5xnvsnTKmw3c97Q?e=6whkRx|Video]] +|22.|  22.11 | Sequential Pattern Miningtiming constraint. Time Series Analysis: Similarities, Distances and Transformations| {{ :magistraleinformatica:dmi:22_time_series_similarity_2023.pdf |}} | [[https://cs.gmu.edu/~jessica/BookChapterTSMining.pdf |Overview on Time Series]]   Recording in Teams Channel  
-|22.|   04.11  09:00-11:00 | FP-Growth - Sequential Pattern Mining | {{ :magistraleinformatica:dmi:17_2021-fp-growth.pdf |}} {{ :magistraleinformatica:dmi:18_sequential_patterns_2021.pdf |}}|Chap. 5 &  Chap. 6 - Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EajQMV7eT3VOnZzh698x08UBKIMNqNdpOFep_1l-43Iprw?e=hR8G5L|Video1]];[[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EfZbzQAqsCBFrHP4MHpx5twBKsoOhZtF6GJ7dKAaYxxWzg?e=ceNFjT|Video2]] +|23.|  23.11  Time Series Analysis: Shapelet & Motif| {{ :magistraleinformatica:dmi:23_time_series_motif-shapelets2023.pdf |}} | {{ :magistraleinformatica:dmi:shaplet.pdf |}} | Recording in Teams Channel   
-|23.|   09.11  09:00-11:00 | Sequential Pattern Mining. Intro to Time Series|Slides on SPM (see previous lecture) |  |  [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/ERRAWko36o1KroWob_cbUMoBZ7wgxVU3NbQK_Yz-jZadog?e=sbOrJR|Video1]];[[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EQy8x2XPoEdBgq-bZWa6DdoBLGwHiWm-4xuzkMuZaGDaRg?e=8iIh7e|Video2]] +|24.|  24.11  Time Series AnalysisShapelet & Motif; introduction to ethics and privacysame slides of the previous lecture and {{ :magistraleinformatica:dmi:19_ethics_privacy_2023_intro.pdf |}}  | {{ :magistraleinformatica:dmi:matrixprofile.pdf |}} [[https://www.cs.ucr.edu/~eamonn/MatrixProfile.html|Papers and resourse on motif]] |  Recording in Teams Channel 
-|24.|   10.11  11:00-13:00 | Time Series Similarities| {{ :magistraleinformatica:dmi:22_time_series_similarity_2022.pdf |}}  | [[https://cs.gmu.edu/~jessica/BookChapterTSMining.pdf| Overview on Time Series]] | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/ER3cHQ0J4YBAsZqo0xzOz4UBefOxnjGELu9SuuMTfo95Eg?e=00wnp0|Video]] +|25.|  29.11 | Python Lab: ARM, SPM, Time series transformations {{ :magistraleinformatica:dmi:ar_spm.zip |}} {{ :magistraleinformatica:dmi:timeseries.zip |}} |  | Recording in Teams Channel 
-|25.|   11.11  09:00-11:00 | Time Series Transformations - Clustering - Classification Slides on transformations (previous lecture) {{ :magistraleinformatica:dmi:23_time_series_motif-2022_2.pdf |}}| |[[https://unipiit.sharepoint.com/:v:/s/a__td_54794/Eff6vI08BjNKkDogZSY6p2kBVNrltI4bZM1d-d8TE0bCdw?e=eqbHKc|Video]]+|26.|  30.11 Python Lab: Time series analysis  | notebooks in the zip file of the previous lecture| | Recording in Teams Channel   
-|26.|   18.11  09:00-11:00 | Shapelets & Motif. Lab: Association Rules Slides on shapelets & motif (previous lecture{{ :magistraleinformatica:dmi:arm-spm.zip |}} | {{ :magistraleinformatica:dmi:matrixprofile.pdf |}}  [[https://www.cs.ucr.edu/~eamonn/MatrixProfile.html|Papers on Matrix Profile]]{{ :magistraleinformatica:dmi:shaplet.pdf |}}|[[https://unipiit.sharepoint.com/:v:/s/a__td_54794/ETW4Mo2LBm9CkSPZljVqplsBxOpJuXJxA5nugiKh-eRZ4Q?e=zjMzVY |Video 1: Shapelets & Motif]]; [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EaH053-TvQpAkdacrfGUDxYBtVPbFdNYJ8IMyhKyKpK3uA?e=xUYRB6|Video 2: Lab ARM]] +|27.|  01.12 | Privacy in AI and Big Data Analytics  | {{ :magistraleinformatica:dmi:19_ethics_privacy2023.pdf |}} This set of slides include alse the introduction of the lecture 24.11.2023 |{{ :magistraleinformatica:dmi:chap-anonymity.pdf |}} {{ :magistraleinformatica:dmi:chap-anonymity.pdf |}} {{ :magistraleinformatica:dmi:prudence.pdf |}} {{ :magistraleinformatica:dmi:chapter-ppdm.pdf |}}| Recording in Teams Channel   
-|27.| 23.11  09:00-11:00 | PythonSequential Pattern Mining & Time Series For SPM see notebooks of previous lecture. {{ :magistraleinformatica:dmi:timeseries-py.zip |}}|   [[https://unipiit.sharepoint.com/:v:/s/Registrazioni628/EdPYuw_W-y9In9AeoHJm1a0BdBeUYhPb6z_3VFZDR9TMCQ?e=bAHAC1|Video]]+|28.|  06.12 | Explainable AI | {{ :magistraleinformatica:dmi:20_explainability_2023.pdf |}}|{{ :magistraleinformatica:dmi:lore-tabular.pdf |}} {{ :magistraleinformatica:dmi:xai-survey.pdf |}} {{ :magistraleinformatica:dmi:imagexai.pdf |}} {{ :magistraleinformatica:dmi:timeseriesxai.pdf |}}| Recording in Teams Channel   
-|28.| 24.11  11:00-13:00 | PythonTime SeriesEthics & Privacy| {{ :magistraleinformatica:dmi:19_ethics_privacy2021.pdf |}} |  | [[https://unipiit.sharepoint.com/:v:/s/Registrazioni628/EQCiARRNIYtNsvZCucPyX30BvP-KDZBXEMcpYFVeS107eQ?e=Jor8lE|Video 1]]; [[https://unipiit.sharepoint.com/:v:/s/Registrazioni628/EeM4qF4IbRVHrQEyEJFhfRYB3HnapYKARglqiZdGfBgEGA?e=1ah2IV| Video 2]]| +|29.|  07.12 Explainable AI | {{ :magistraleinformatica:dmi:21_anomaly_detection_2023.pdf |}} {{ :magistraleinformatica:dmi:anomaly_detection.zip |}}| | Recording in Teams Channel   
-|29.| 25.11  09:00-11:00 | Privacy  | same slides off the last lecture |  | [[https://unipiit.sharepoint.com/:v:/s/Registrazioni628/ETMsw1ltw1RMhxXTIRLrb18BTNOx5koRTvQllhM295EDgg?e=wEgRnp|Video]]+|30.|  13.12 | Anomaly Detection | {{ :magistraleinformatica:dmi:21_anomaly_detection_2023.pdf |}} | | Recording in Teams Channel   
-|30.| 30.11  09:00-11:00 |Explainability | {{ :magistraleinformatica:dmi:20_explainability_2021.pdf |}} |  [[|Video]]+|31-32.|  14.12 9-11| Lab Python in AD + Lab Python in XAI| {{ :magistraleinformatica:dmi:anomaly_detection.zip |}}| | Recording in Teams Channel   
-|31.| 01.12  11:00-13:00 |Anomaly Detection + Python: XAI | {{ :magistraleinformatica:dmi:lezione-xai.zip |XAI Notebook}} |  [[|Video]]+|33.|  15.12 9-11| Lab Python in XAI + Paper Presentation| | |    
-|32.| 02.12  09:00-11:00 |PythonXAI + AD {{ :magistraleinformatica:dmi:anomalydetection-1.ipynb.zip | Anomaly Detection Notebook}}|  [[|Video]]+|34.|  18.12 09-11| Paper Presentation| | |    
-|33.| 07.12  09:00-11:00 |Paper Presentation|   | | +|35.|  20.12 09-11| Paper Presentation| | |    
-|34.| 09.12  09:00-11:00 |Paper Presentation|   | | +|36.|  21.12 09-11| Paper Presentation| | |    |
-|35.| 14.12  09:00-11:00 |Paper Presentation|   | | +
-|36.| 15.12  11:00-13:00 |Paper Presentation|   | | +
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 ====== Exams ====== ====== Exams ======
 **Project ** **Project **
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 The project has to be performed by a team of 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. The project has to be performed by a team of 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:projectdescriptiondm2022-new.pdf | Project Description}} +  * First part of the project consists in the **assignments** described here: {{ :magistraleinformatica:dmi:project_description_dm23-pub.pdf | Project Description}} 
-  - **Dataset:[[https://unipiit.sharepoint.com/:u:/s/a__td_54794/ERsHd0L8ZbtCvAjWsHdzmfkBb-B2EvkiQLU09e22b0xsTQ?e=VfSaNW|Twitter Data]]**  +  - **Dataset: {{ :magistraleinformatica:dmi:gun-data.zip Dataset Files}}**  
-  - **Deadline**: the fist part has to be delivered within  November <del>5th 2022</del> 122022. Send an email to: anna.monreale@unipi.it, francesca.naretto@sns.it, lorenzo.mannocci@phd.unipi.it+  - **Deadline**: the fist part has to be delivered within <del> November 19th, 2023</del> November 26th2023. Send an email to: anna.monreale@unipi.it, lorenzo.mannocci@phd.unipi.it
    
-  * Second part of the project consists in the assignment described here: {{ :magistraleinformatica:dmi:project_description_dm2022-updated.pdf |Updated Project Description}} +  * Second part of the project consists in the assignment described here: {{ :magistraleinformatica:dmi:project_description_dm23-pub-updated.pdf |Updated Project Description}} 
-     - **Deadline**: Jan 8, 2023+     - **Deadline**: Jan 8, 2024 
  
-  * Third part of the project consists in the assignment described here: {{ :magistraleinformatica:dmi:project_description_dm2022-complete.pdf |Complete Project Description}} +  * Third part of the project consists in the assignment described here: {{ :magistraleinformatica:dmi:project_description_dm23-pub-complete.pdf |Updated Project Description}} 
-   - Note that the document contains also rules for the delivery and final exam! +   - **Deadline**:   Jan 8, 2024 
-   - **Deadline**:   Jan 8, 2023+
  
  
-**Students who did not deliver the above project within **Jan 8, 2023** 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. **+**Students who did not deliver the above project within **Jan 8, 2024** need to ask by email a new project to the teachers. The project that will be assigned will require about 20 days 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). The paper presentation can be done by the group or by a single person.+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 on the entire program. They only need to present the project (see next point) and answer open question only on the topics which will not be covered by the project. The paper presentation can be done by the group or by a single person.
  
 **Oral Exam** **Oral Exam**
-  * **Project presentation** (with slides) – 10-15 minutes: mandatory for all the students +  * **Project presentation** (with slides) – 10-15 minutes: mandatory for all the students with question fo understanding the details of any part of the project. 
-  * ** Open questions ** on the entire program: optional only for students opting for paper presentation. +  * ** Open questions on the entire program **for students who will not opt for paper presentation 
- +  * ** Open questions on the topics which will not be covered by the project ** only for students opting  for paper presentation. 
 +  * Group presentations of the project are preferred. If this is impossible please contact me for finding a solution. 
 + 
 +**How to book for the exam colloquium? ** 
 +  
 +In https://esami.unipi.it/ you can find the dates for the exam: one for January and one for February. Each student must do the registration on one of the 2 dates. These are not the dates of the colloquium or project delivery but we will use the list of registered students for organizing the exam dates. After that deadline we will share with you a calendar for the oral exam. 
   
  
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 ====== Previous years ===== ====== Previous years =====
 +[[DM-INF 2022-2023]]
 +
 [[DM-INF 2021-2022]] [[DM-INF 2021-2022]]
  
magistraleinformatica/dmi/start.1670595234.txt.gz · Ultima modifica: 09/12/2022 alle 14:13 (16 mesi fa) da Anna Monreale