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dm:start [08/08/2024 alle 12:38 (16 mesi fa)] – Salvatore Ruggieridm:start [01/12/2025 alle 10:55 (12 giorni fa)] (versione attuale) – [News] Riccardo Guidotti
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-====== Data Mining A.A. 2023/24 ======+====== Data Mining A.A. 2025/26 ======
  
 ===== DM1 - Data Mining: Foundations (6 CFU) ===== ===== DM1 - Data Mining: Foundations (6 CFU) =====
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 Teaching Assistant Teaching Assistant
-  * **Andrea Fedele**+  * **Alessio Cascione**
     * KDDLab, Università di Pisa     * KDDLab, Università di Pisa
-    * [[https://www.linkedin.com/in/andrea-fedele/?originalSubdomain=it]] +    * [[https://www.linkedin.com/in/alessio-cascione-a77224159/?originalSubdomain=it]] 
-    * [[andrea.fedele@phd.unipi.it]]  +    * [[alessio.cascione@phd.unipi.it]]   
 ===== DM2 - Data Mining: Advanced Topics and Applications (6 CFU) ===== ===== DM2 - Data Mining: Advanced Topics and Applications (6 CFU) =====
  
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 Teaching Assistant Teaching Assistant
-  * **Andrea Fedele**+  * **Alessio Cascione**
     * KDDLab, Università di Pisa     * KDDLab, Università di Pisa
-    * [[https://www.linkedin.com/in/andrea-fedele/?originalSubdomain=it]] +    * [[https://www.linkedin.com/in/alessio-cascione-a77224159/?originalSubdomain=it]] 
-    * [[andrea.fedele@phd.unipi.it]]   +    * [[alessio.cascione@phd.unipi.it]]   
-    * Meeting: https://calendly.com/andreafedele/+
 ====== News ====== ====== News ======
 +     * **[01.12.2025] The lecture of Thursday 04/12/2025 is moved to Friday 05/12/2025 9-11 in room C (project presentation of Prof.ssa Pierotti will start at 11 after DM lecture). The last lecture will be held on Tuesday 09/12/2025 9-11 in room M1 (as Monday 08/12/2025 is holiday), while the lecture of P4DS is moved to 09/12/2025 16-18 in room C1**.
 +     * [19.11.2025] The lecture of Thursday 20/11/2025 will be held in room N1 due to not usability of room E. 
 +     * [07.10.2025] The lecture of Thursday 10/10/2025 is canceled due to the UniPi Orienta event. The recovery lecture is Tuesday 14/10/2025 9-11 room M1. 
 +     * [06.10.2025] Link to Project Groups Registration DM1 [25/26] (max 3 students for each group - access with your University of Pisa account, deadline 17/10/2025:)  [[https://docs.google.com/spreadsheets/d/1JX3VRwcZZFcTdpiguEwPsR_p4gDyRd7J89O84J7AeyY/edit?gid=0#gid=0| Link]]
 +     * [28.07.2025] Lectures will start on Monday 29 September 2025 at 09.00 room E. Lectures will be in presence only. Registrations of the lectures of past years can be found at the bottom of this web page.
 +    
 +     
 +
 +----
  
-     * **[24.05.2024]** When registering for the oral exam please specify in the notes DM1 if you do not want to do DM2 (that is assumed by default). After having booked it please contact Prof. Pedreschi to agree on the exam date (put Prof. Guidotti and Andrea Fedele in cc). There will be no agenda for DM1. 
-     * [03.05.2024] Next lecture of DM2 will be as usual on Monday 06/05 from 9 to 11 in room C. 
-     * [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. 
-     * [13.10.2023] To schedule meeting with the Teaching Assistant you can use: https://calendly.com/andreafedele/ 
-     * [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) 
-     * [20.09.2023] Thursday 21 September there will be no lecture. 
-     * [11.09.2023] Lectures will start on Monday 18 September 2023 at 11.00 room C1. 
-     * [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. 
-     * [11.09.2023] Project Groups [[https://docs.google.com/spreadsheets/d/10R5AcqdlXsqTAxSys6zyqArvdytq4HH6Ik8Uy-NHkQ4/edit?usp=sharing|link]] 
-     * [11.09.2023] MS Teams [[https://teams.microsoft.com/l/team/19%3a7uEgK_aekrBFuOsbREccAa-tfqeSwvfBemfK_lG6HA01%40thread.tacv2/conversations?groupId=84cc4fec-41fc-4208-a9d4-a02675216d22&tenantId=c7456b31-a220-47f5-be52-473828670aa1|link]]  
 ====== Learning Goals ====== ====== Learning Goals ======
   * DM1   * DM1
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 ^  Day of Week  ^  Hour  ^  Room  ^  ^  Day of Week  ^  Hour  ^  Room  ^ 
-|  Monday  |  11:00 - 13:00  |  C1   |  +|  Monday  |  09:00 - 11:00  |  E   |  
-|  Wednesday  |  11:00 - 13:00  |  C1  | +|  Thursday  |  09:00 - 11:00  |  E  | 
  
 **Office hours - Ricevimento:** **Office hours - Ricevimento:**
  
   * Prof. Pedreschi   * Prof. Pedreschi
-      * Monday 16:00 - 18:00 +      * Monday 15:00-17:00 or Appointment by email 
-      * Online+      * Room 318 Dept. of Computer Science or MS Teams 
   * Prof. Guidotti   * Prof. Guidotti
-      * Tuesday 16:00 - 18:00 or Appointment by email+      * Thursday 16:00 - 18:00 or Appointment by email
       * Room 363 Dept. of Computer Science or MS Teams       * Room 363 Dept. of Computer Science or MS Teams
 +
 +
 +  * Alessio Cascione
 +      * Google Meet slot - https://calendly.com/alessio-cascione-phd/30min 
 +      * Alternative appointment by email
 +      * I will be out of office from 05/12/2025 to 15/12/2025, checking emails and answering  sporadically. 
  
      
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 ^  Day of Week  ^  Hour  ^  Room  ^  ^  Day of Week  ^  Hour  ^  Room  ^ 
-|  Monday   |  09:00 - 11:00  |  C   |  +|  Monday   |  11:00 - 13:00  |  E   |  
-|  Wednesday  |  11:00 - 13:00  |  C  |  +|  Wednesday  |  09:00 - 11:00  |  E  |  
  
 **Office Hours - Ricevimento:** **Office Hours - Ricevimento:**
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   * 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's authors [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php#item4|Slides per "Introduction to Data Mining"]].   * 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's authors [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php#item4|Slides per "Introduction to Data Mining"]].
        
 +   
 +===== FAQ =====
  
-  +For the academic year 2025/2026, we make available a document containing **frequently asked questions (FAQs)** about the project at the end of the lecture. 
 +Please consult this document first, as your question may already be answered there. 
 +The FAQ will be updated regularly after each lecture with new relevant questions from students. 
 + 
 +Check the document: 
 +https://docs.google.com/document/d/1OLa02xofxRPj1zUJ7zm_boxL_ZeAFR1HWCB4lgozgz8/edit?usp=sharing 
 + 
 + 
 + 
 +===== Recording past years ===== 
 + 
 +Link to past years recordings (incrementally updated with respect to the current lectures of the course) 
 + 
 +https://unipiit-my.sharepoint.com/:f:/g/personal/a_cascione_studenti_unipi_it/IgCdnqZe6wTKQJR_4yVrXE3gAcmqWHBSxvxW0HtsA596LWQ?e=OCa34K
 ===== Software===== ===== Software=====
  
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   * Didactic Data Mining [[http://matlaspisa.isti.cnr.it:5055/Help| DDMv1]], [[https://kdd.isti.cnr.it/ddm/#/| DDMv2]]    * Didactic Data Mining [[http://matlaspisa.isti.cnr.it:5055/Help| DDMv1]], [[https://kdd.isti.cnr.it/ddm/#/| DDMv2]] 
    
-====== Class Calendar (2023/2024) ======+====== Class Calendar (2025/2026) ======
  
 ===== First Semester (DM1 - Data Mining: Foundations) ===== ===== First Semester (DM1 - Data Mining: Foundations) =====
  
 ^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^ ^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^
-|01.| 18.09.2023 | 11-13 |C1| Overview, Introduction | {{ :dm:00_dm1_introduction_2023_24.pdf | Intro}} | Pedreschi| +|   | 15.09.2025 |          |  | No Lecture |  |  | 
-|   | 20.09.2023 | 11-13 |  | No Lecture |  |  | +|   | 18.09.2025 | |  | No Lecture |  |  | 
-|02.| 25.09.2023 | 11-13 |C1| Lab. Introduction to Python | {{ :dm:dm1_lab01_python_basics.zip | Python Basic}} | Guidotti| +|   | 22.09.2025 | |  | No Lecture |  |  | 
-|03.| 27.09.2023 | 11-13 |C1| Lab. Data Understanding | {{ :dm:dm1_lab02_data_understanding.zip | Data Understanding}} | Guidotti| +|   | 25.09.2025 | |  | No Lecture |  |  | 
-|04.| 02.10.2023 | 11-13 |C1| Data Understanding | {{ :dm:01_dm1_data_understanding_2023_24.pdf | Data Understanding}} | Guidotti| +|01.| 29.09.2025 | 09-11 | E | Overview, Introduction | {{ :dm:00_dm1_introduction_2025_26.pptx.pdf | Intro}} | Pedreschi | 
-|05.| 04.10.2023 | 11-13 |C1| Data Understanding & Preparation | {{ :dm:01_dm1_data_understanding_2023_24.pdf | Data Understanding}}, {{ :dm:02_dm1_data_preparation_2023_24.pdf | Data Preparation}} | Pedreschi| +|02.| 02.10.2025 | 09-11 | E | The KDD process | {{ :dm:00_dm1_introduction_2025_26.pptx.pdf | Intro}} | Pedreschi | 
-|06.| 09.10.2023 | 11-13 |C1| Data Preparation & Data Similarity | {{ :dm:02_dm1_data_preparation_2023_24.pdf | Data Preparation}}, {{ :dm:03_dm1_data_similarity_2023_24.pdf | Data Similarity}} | Pedreschi| +|03.| 06.10.2025 | 09-11 | E | Introduction to Python | {{:dm:06.10.25_python_basic_2025_lecture_in_class.zip |}} | Pedreschi, Cascione | 
-|07.| 11.10.2023 | 11-13 |C1| Data Similarity & Lab. Data Understanding | {{ :dm:03_dm1_data_similarity_2023_24.pdf | Data Similarity}}, {{ :dm:dm1_lab02_data_understanding.zip | Data Understanding}} | Pedreschi| +|   | 09.10.2025 |  |  | No Lecture (UNIPI Orienta) |  |  | 
-|08.| 16.10.2023 | 11-13 |C1| Introduction to Clustering, K-Means | {{ :dm:04_dm1_clustering_intro_2023_24.pdf | Intro_Clustering}}, {{:dm:05_dm1_kmeans_2023_24.pdf | K-Means }} | Pedreschi| +|04.| 13.10.2025 | 09-11 | E | Data Understanding | {{ :dm:01_dm1_data_understanding_2025_26.pdf | Data Understanding }} | Pedreschi | 
-|09.| 18.10.2023 | 11-13 |C1| Clustering Validation, Hierarchical Clustering | {{ :dm:04_dm1_clustering_intro_2023_24.pdf | Intro_Clustering}}, {{ :dm:06_dm1_hierarchical_clustering_2023_24.pdf | Hierarchical}} | Pedreschi| +|05.| 14.10.2025 | 09-11 | C1 | Data Preparation | {{ :dm:02_dm1_data_preparation_2025_26.pdf | Data Preparation}}, {{ :dm:03_dm1_data_similarity_2025_26.pdf | Data Similarity}} | Guidotti | 
-|10.| 23.10.2023 | 11-13 |C1| Density-based Clustering | {{ :dm:07_dm1_density_based_2023_24.pdf | Density-based Clustering}} | Pedreschi| +|04.| 16.10.2025 | 09-11 | E | Data Understanding Lab| {{ :dm:16.10.25_data_understanding_2025_lecture_in_class.zip |}} | Guidotti, Cascione | 
-|11.| 25.10.2023 | 11-13 |C1| Lab. Clustering | {{ :dm:dm1_lab03_clustering.zip | Clustering}}| Guidotti| +|06.| 20.10.2025 | 09-11 | E | Data Similarity and Introduction to Clustering | {{ :dm:03_dm1_data_similarity_2025_26.pdf | Data Similarity}}, {{ :dm:04_dm1_clustering_intro_2025_26.pdf | Introduction to Clustering}} | Guidotti | 
-|12.| 30.10.2023 | 11-13 |C1| Ex. Clustering | {{ :dm:ex1_dm1_clustering_2023_24.pdf | ExClustering}}| Guidotti| +|07.| 23.10.2025 | 09-11 | E | Centroid-based Clustering Algorithm | {{ :dm:05_dm1_kmeans_2025_26.pdf | Centroid-based Clustering}} | Guidotti | 
-|   | 01.11.2023 | 11-13 |  | No Lecture |  |  | +|08.| 27.10.2025 | 09-11 | E | Hierarchical Clustering Algorithm | {{ :dm:06_dm1_hierarchical_clustering_2025_26.pdf | Hierarchical Clustering}} | Guidotti | 
-|13.| 06.11.2023 | 11-13 |C1| Intro Classification, kNN[[https://unipiit.sharepoint.com/sites/a__td_61280/Shared%20Documents/General/Recordings/Lecture%2006_11_2023-20231106_110052-Registrazione%20della%20riunione.mp4?web=1|(video)]] | {{ :dm:08_dm1_classification_intro_2023_24.pdf | Intro_Classification}}, {{ :dm:09_dm1_knn_2023_24.pdf | kNN}}| Guidotti| +|09.| 27.10.2025 | 09-11 | E | Density-based Clustering Algorithm | {{ :dm:07_dm1_density_based_2025_26.pdf | Density-based Clustering}} | Guidotti | 
-|14.| 08.11.2023 | 11-13 |C1| Naive Bayes, Exercises | {{ :dm:10_dm1_naive_bayes_2023_24.pdf | Naive Bayes}} | Guidotti| +|10.|03.11.2025 | 09-11 | E | Clustering Lab | {{ :dm:03.11.25_clustering_2025_lecture_in_class.zip |}} | Pedreschi, Cascione | 
-|15.| 13.11.2023 | 11-13 |C1| Model Evaluation | {{ :dm:11_dm1_classification_eval_2023_24.pdf | Model Evaluation}} | Guidotti| +|11.|04.11.2025 | 09-11 | C1 | Classification: Overview and K-Nearest Neighbours | {{ :dm:08_dm1_classification_intro_2024_25.pptx.pdf | Classification Overview }} {{ :dm:09_dm1_knn_2024_25.pptx.pdf | KNN Classifier }} | Pedreschi | 
-|16.| 15.11.2023 | 11-13 |C1| Model Evaluation Exercises & Lab | {{ :dm:dm1_lab04_classification_regression.zip | Classification}} | Guidotti| +|12.|06.11.2025 | 09-11 | E | Classification: Naive Bayes Classifier and Exercises | {{ :dm:10_dm1_naive_bayes_2024_25.pptx.pdf | Naive Bayes }} | Pedreschi | 
-|   | 20.11.2023 | 11-13 |  | No Lecture |  |  | +|13.|10.11.2025 | 09-11 | E | Classification: Evaluation | {{ :dm:11_dm1_classification_eval_2024_25.pptx.pdf | Model evaluation }} | Pedreschi | 
-|17.| 22.11.2023 | 11-13 |C1| Decision Tree Classifier | {{ :dm:12_dm1_decision_trees_2023_24.pdf | Decision Tree}} | Pedreschi| +|14.|13.11.2025 | 09-11 | E | Classification: Decision Trees (1) | {{ :dm:12_dm1_decision_trees_2024_25.pptx.pdf | Decision trees }} | Pedreschi | 
-|18.| 27.11.2023 | 11-13 |C1| Decision Tree Classifier | {{ :dm:12_dm1_decision_trees_2023_24.pdf | Decision Tree}} | Pedreschi| +|15.|17.11.2025 | 09-11 | D5 | Classification: Decision Trees (2) |  | Pedreschi | 
-|19.| 29.11.2023 | 11-13 |C1| Exercises and Lab. Decision Tree Classifier | {{ :dm:dm1_lab04_classification.zip | Decision Tree}} | Guidotti| +|16.|18.11.2025 | 09-11 | C1 | Classification: Decision Trees (3) |  | Pedreschi | 
-|20.| 04.12.2023 | 11-13 |C1| Decision Tree Classifier, Exercises and Lab | {{ :dm:12_dm1_decision_trees_2023_24.pdf | Decision Tree}} | Pedreschi| +|17.|20.11.2025 | 09-11 | N1 | Classification Lab | {{ :dm:20.11.25_classification_2025_lecture_in_class.zip |}} | Guidotti, Cascione | 
-|21.| 06.12.2023 | 11-13 |C1| Intro Regression & Lab. Regression | {{ :dm:12_dm1_linear_regression_2023_24.pdf | Regression}}, {{ :dm:dm1_lab05_regression.zip | Regression}} | Guidotti| +|18.|24.11.2025 | 09-11 | E | Pattern Mining: Apriori | {{ :dm:14_dm1_pattern_mining_2024_25.pptx.pdf | Pattern mining & association rules }} | Pedreschi | 
-|22.| 11.12.2023 | 11-13 |C1| Into Pattern Mining and Apriori | {{ :dm:13_dm1_pattern_mining_2023_24.pdf | Pattern Mining}} | Pedreschi| +|19.|25.11.2025 | 09-11 | C | Pattern Mining: Lift, Interest, Multiattribute |  | Pedreschi | 
-|23.| 13.12.2023 | 16-18 |C1| Apriori & Lab. Pattern Mining | {{ :dm:13_dm1_pattern_mining_2023_24.pdf | Pattern Mining}}, {{ :dm:dm1_lab06_pattern_mining.zip | Pattern Mining}}  | Pedreschi| +|20.|27.11.2025 | 09-11 | E | Regression: Problem, Linear, KNN, Decision Tree | {{ :dm:13_dm1_linear_regression_2024_25.pptx.pdf | Regression }} | Pedreschi | 
-|24.| 18.12.2023 | 11-13 |C| FP-Growth and Exercises | {{ :dm:13_dm1_pattern_mining_2023_24.pdf | Pattern Mining}} | Guidotti|+|21.|01.12.2025 | 09-11 | E | Lab on Regression and Pattern Mining; FPGROWTH| {{ :dm:01.12.25_regression_2025_lecture_in_class.zip |}}, {{ :dm:01.12.25_pattern_mining_2025_lecture_in_class.zip |}}, {{ :dm:14_dm1_pattern_mining_2024_25.pptx.pdf | FPGROWTH }}| Guidotti, Cascione | 
 + 
 ===== Second Semester (DM2 - Data Mining: Advanced Topics and Applications) ===== ===== Second Semester (DM2 - Data Mining: Advanced Topics and Applications) =====
  
 ^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^ ^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^
-|01.| 19.02.2024 | 14-16 |C| Overview, Rule-based Models | {{ :dm:14_dm2_intro_2023_24.pdf | Introduction}}, {{ :dm:dm2_project_guidelines_23_24.pdf | Guidelines}}, {{ :dm:15_dm2_rule_based_classifier_2023_24.pdf | Rule-based Models }} | Guidotti| +|01.| 18.02.2025 | 14-16 |A1| Overview, Imbalanced Learning | {{ :dm:16_dm2_intro_2024_25.pdf | Introduction}}, {{ :dm:dm2_project_guidelines_24_25.pdf | Guidelines}}, {{ :dm:17_dm2_imbalanced_learning_2024_25.pdf | Imbalanced Learning}}, [[https://unipiit.sharepoint.com/:v:/s/a__td_64992/EWrX2F6xAS9JtNXh1l5JIgMByAU0eMWBFr5sbGIYL3jakA|Link]] | Guidotti| 
-|   | 21.02.2024 |  | | No Lecture |  |  | +
-|   | 26.02.2024 |  | | No Lecture |  |  | +
-|02.| 28.02.2024 | 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 | {{ :dm:16_dm2_sequential_pattern_mining_2023_24.pdf | Sequential Pattern Mining}}, {{ :dm:GSP.zip | GSP}} | Guidotti| +
-|04.| 06.03.2024 | 11-13 |C| Transactional Clustering | {{ :dm:17_dm2_transactional_clustering_2023_24.pdf | Transactional Clustering}} | Guidotti| +
-|05.| 11.03.2024 | 9-11 |C| Time Series Similarity | {{ :dm:18_dm2_time_series_similarity_2023_24.pdf | Time Series Similarity}}, {{ :dm:dm2_lab00_spotify.zip | TS_Load}}, {{ :dm:dm2_lab01_dist_transf.zip | TS_Similarity}} | Guidotti| +
-|06.| 13.03.2024 | 11-13 |C| Time Series Approximation | {{ :dm:19_dm2_time_series_clustering_approximation_2023_24.pdf | Time Series Clustering}}, {{ :dm:dm2_lab02_approx_clust.zip | TS_Approx_Clustering}} | Guidotti| +
-|07.| 18.03.2024 | 9-11 |C| Time Series Clustering & Motifs| {{ :dm:20_dm2_time_series_matrix_profile_2023_24.pdf | Time Series Motifs}}, {{ :dm:dm2_lab03_motifs.zip | TS_Motifs}} | Guidotti| +
-|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 | 9-11 |C| Imbalanced Learning | {{ :dm:22_dm2_imbalanced_learning_2023_24.pdf | Imbalanced Learning}}, {{ :dm:dm2_lab05_imbalance.zip |ImbLearn}} | Guidotti|  +
-|10.| 27.03.2024 | 11-13 |C| Dimensionality Reduction | {{ :dm:23_dm2_dimred_2023_24.pdf | Dimensionality Reduction}}, {{ :dm:dm2_lab06_dimred.zip |DimRed}} | Guidotti| +
-|11.| 03.04.2024 | 11-13 |C| Outlier Detection | {{ :dm:24_dm2_anomaly_detection_2023_24.pdf | Outlier Detection}} | Guidotti| +
-|12.| 08.04.2024 | 9-11 |C| Outlier Detection | {{ :dm:24_dm2_anomaly_detection_2023_24.pdf | Outlier Detection}}, {{ :dm:dm2_lab07_outlier_det.zip | OutlierDetection}} | Guidotti| +
-|13.| 10.04.2024 | 11-13 |C| Outlier Detection | {{ :dm:24_dm2_anomaly_detection_2023_24.pdf | Outlier Detection}}, {{ :dm:dm2_lab07_outlier_det.zip | OutlierDetection}} | Guidotti| +
-|14.| 15.04.2024 | 14-16 |C| Gradient Descend, MLE | {{ :dm:25_dm2_gradient_descent_2023_24.pdf | GD}}, {{ :dm:26_dm2_maximum_likelihood_estimation_2023_24.pdf | MLE}} | Guidotti| +
-|15.| 17.04.2024 | 11-13 |C| Odds, LogOdds, Logistic Regression| {{ :dm:27_dm2_odds_2023_24.pdf | Odds}}, {{ :dm:28_dm2_logistic_regression_2023_24.pdf | LogReg}}, {{ :dm:dm2_lab08_logistic_reg.zip | LogReg}} | Guidotti| +
-|16.| 22.04.2024 | 9-11 |C| Support Vector Machine | {{ :dm:29_dm2_svm_2023_24.pdf | SVM}}, {{ :dm:dm2_lab09_svm.zip | SVM}} | Guidotti| +
-|17.| 24.04.2024 | 11-13 |C| Perceptron, Neural Networks| {{ :dm:30_dm2_perceptron_2023_24.pdf | Perceptron}} | Guidotti| +
-|18.| 29.04.2024 | 9-11 |C| Deep Neural Networks | {{ :dm:31_dm2_neural_network_2023_24.pdf | Deep Neural Networks}}, {{ :dm:dm2_lab10_neural_networks.zip | NN}} | Guidotti| +
-|19.| 06.05.2024 | 9-11 |C| CNN, RNN, DL-TS, Ensemble Intro | {{ :dm:31_dm2_neural_network_2023_24.pdf |DNN}}, {{ :dm:21_dm2_time_series_classification_2023_24.pdf | TSC-DNN}}, {{ :dm:32_dm2_ensemble_2023_24.pdf | Ensemble}} | Guidotti| +
-|20.| 08.05.2024 | 11-13 |C| Ensemble, Boosting, Adaboost | {{ :dm:32_dm2_ensemble_2023_24.pdf | Ensemble}}, {{ :dm:dm2_lab11_ensamble.zip | LabEnsemble}} | Guidotti| +
-|21.| 13.05.2024 | 9-11 |C| Ensemble-TS, Gradient Boosting | {{ :dm:33_dm2_gradient_boost_2023_24.pdf | Gradient Boosting Machines}}, {{ :dm:dm2_lab11_ensamble.zip | LabEnsemble}}  | Guidotti| +
-|22.| 15.05.2024 | 11-13 |C| Extreme Gradient Boosting | {{ :dm:33_dm2_gradient_boost_2023_24.pdf | Gradient Boosting Machines}}, {{ :dm:dm2_lab11_ensamble.zip | LabEnsemble}} | Guidotti| +
-|23.| 20.05.2024 | 9-11 |C1| eXplainable Artificial Intelligence | {{ :dm:34_dm2_explainability_2023_24.pdf | XAI}}, {{ :dm:dm2_lab12_xai.zip | LabXAI}} | Guidotti| +
-|24.| 22.05.2024 | 11-13 |C1| eXplainable Artificial Intelligence | {{ :dm:34_dm2_explainability_2023_24.pdf | XAI}}, {{ :dm:dm2_lab12_xai.zip | LabXAI}}  | Guidotti|+
 ====== Exams ====== ====== Exams ======
  
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   - 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.   - 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.
   - Understanding of the algorithms illustrated during the course and their practical implementation. You will be asked to perform one or more simple exercises. The text will be shown on the teacher's screen and / or copied to Miro. The student will have to use pen and paper (if online by Miro https://miro.com/ to show how the exercise is solved.   - Understanding of the algorithms illustrated during the course and their practical implementation. You will be asked to perform one or more simple exercises. The text will be shown on the teacher's screen and / or copied to Miro. The student will have to use pen and paper (if online by Miro https://miro.com/ to show how the exercise is solved.
-  - Discussion of the project with questions from the teacher regarding unclear aspects, +  - Discussion of the project with questions from the teacher regarding unclear aspects, questionable steps or choices.
-questionable steps or choices.+
  
 ** Final Mark: ** for 12-credit exam, the final mark will be obtained as the ** Final Mark: ** for 12-credit exam, the final mark will be obtained as the
 average mark of DM1 and DM2. average mark of DM1 and DM2.
 +
 +*** Exams Registration Instructions for DM1***
 +- Use the Google registration form: TBD if you cannot register on Esami on Data Mining for year 2025/2026. 
 +- When the registration closes you will receive a link to the Agenda
 +- Register on the Agenda selecting day and time (do not change you choice or cancel, if you book you want to do the exam)
 +- Submit the project at least 1 week before the day you selected (or within 31/12 to get +0.5 extra mark)
  
 ===== Exam Booking Periods ===== ===== Exam Booking Periods =====
   * Exam portal link: [[https://esami.unipi.it/|here]]   * Exam portal link: [[https://esami.unipi.it/|here]]
-  * 1st Appello: from 09/01/2024 to 31/12/2024 +  * Registration Form: TBD 
-  * 2nd Appello: from 01/02/2024 to 17/02/2024 +  * 1st Appello: from TBD to TBD 
-  * 3rd Appello: from 05/05/2024 to 30/05/2024  +  * 2nd Appello: from TBD to TBD 
-  * 4th Appello: from 02/06/2024 to 27/06/2024  +  * 3rd Appello: from TBD to TBD 
-  * 5th Appello: from 19/06/2024 to 14/07/2024  +  * 4th Appello: from TBD to TBD 
-  * 6th Appello: +  * 5th Appello: from TBD to TBD 
 +  * 6th Appello: from TBD to TBD
    
-===== Exam Booking Agenda ===== 
-When registering for the oral exam please specify in the notes DM1 if you do not want to do DM2 (that is assumed by default). After having booked for DM1 please contact Prof. Pedreschi to agree on the exam date (put Prof. Guidotti and Andrea Fedele in cc). There will be no agenda for DM1. 
  
-  * 1st Appello - DM1: https://agende.unipi.it/yra-ief-dmo, DM2: https://agende.unipi.it/rnm-urj-wsu 
-  * 2nd Appello - DM1: https://agende.unipi.it/yra-ief-dmo, DM2: https://agende.unipi.it/rnm-urj-wsu 
-  * 3rd Appello: - DM1 & DM2: from 04/06/2024 to 13/06/2024 (deliver project by 29/05/2024)  
-  * 4th Appello: - DM1 & DM2: from 02/07/2024 to 11/07/2024 (deliver project by 25/06/2024)  
-  * 5th Appello: - DM1 & DM2: from 22/07/2024 to 25/07/2024 (deliver project by 15/07/2024) 
-  * 6th Appello:  
- 
-**Do not forget to make the evaluation of the course!!!** 
 ===== Exam DM1 ====== ===== Exam DM1 ======
  
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   * An **oral exam**, that includes: (1) discussing the project report; (2) discussing topics presented during the classes, including the theory and practical exercises.    * An **oral exam**, that includes: (1) discussing the project report; (2) discussing topics presented during the classes, including the theory and practical exercises. 
  
-  * A **project**, that consists in exercises requiring the use of data mining tools for analysis of data. Exercises include: data understanding, clustering analysis, pattern mining, and classification (guidelines will be provided for more details). The project has to be performed by min 2, max 3 people. It has to be performed by using Python or any other data mining software. The results of the different tasks must be reported in a unique paper. The total length of this paper must be max 20 pages of text including figures. The paper must be emailed to [[andrea.fedele@phd.unipi.it]] and [[riccardo.guidotti@unipi.it]]. Please, use “[DM1 2023-2024] Project” in the subject.+  * A **project**, that consists in exercises requiring the use of data mining tools for analysis of data. Exercises include: data understanding, clustering analysis, pattern mining, and classification (guidelines will be provided for more details). The project has to be performed by min 2, max 3 people. It has to be performed by using Python or any other data mining software. The results of the different tasks must be reported in a unique paper. The total length of this paper must be max 20 pages of text including figures. The paper must be emailed to [[alessio.cascione@phd.unipi.it]] and [[riccardo.guidotti@unipi.it]]. Please, use “[DM1 2025-2026] Project” in the subject.
    
   * **Dataset**   * **Dataset**
-    - Assigned: 25/09/2023 +    - Assigned: 15/10/2025 
-    - MidTerm Submission: 15/11/2023 (+0.5) (half project required, i.e., Data Understanding & Preparation and Clustering) +    - MidTerm Submission: 15/11/2025 (+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). +    - Final Submission: 31/12/2025 (+0.5) one week before the oral exam (complete project required). 
-    - Dataset: {{ :dm:std.zip | STD}}+    - Dataset: Download here {{ :dm:dm1_25_26_dataset.zip |}}
  
 ** DM1 Project Guidelines ** ** DM1 Project Guidelines **
-See {{ :dm:dm1_project_guidelines_23_24.pdf | Project Guidelines}}.+See {{ :dm:dm1_project_guidelines_25_26.pdf |}}
  
  
- 
- 
-  
 ===== Exam DM2 ====== ===== Exam DM2 ======
  
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   * An **oral exam**, that includes: (1) discussing the project report; (2) discussing topics presented during the classes, including the theory and practical exercises.    * An **oral exam**, that includes: (1) discussing the project report; (2) discussing topics presented during the classes, including the theory and practical exercises. 
  
-  * A **project**, that consists in exercises requiring the use of data mining tools for analysis of data. Exercises include: imbalanced learning, dimensionality reduction, outlier detection, advanced classification/regression methods, time series analysis/clustering/classification (guidelines will be provided for more details). The project has to be performed by min 1, max 3 people. It has to be performed by using Python or any other data mining software. The results of the different tasks must be reported in a unique paper. The total length of this paper must be max 30 pages of text including figures. The paper must be emailed to [[andrea.fedele@phd.unipi.it]] and [[riccardo.guidotti@unipi.it]]. Please, use “[DM2 2023-2024] Project” in the subject.+  * A **project**, that consists in exercises requiring the use of data mining tools for analysis of data. Exercises include: imbalanced learning, dimensionality reduction, outlier detection, advanced classification/regression methods, time series analysis/clustering/classification (guidelines will be provided for more details). The project has to be performed by min 1, max 3 people. It has to be performed by using Python or any other data mining software. The results of the different tasks must be reported in a unique paper. The total length of this paper must be max 30 pages of text including figures. The paper must be emailed to [[andrea.fedele@phd.unipi.it]] and [[riccardo.guidotti@unipi.it]]. Please, use “[DM2 2024-2025] Project” in the subject.
    
   * **Dataset**   * **Dataset**
-    - Assigned: 19/02/2024 +    - Assigned: 18/02/2026 
-    - MidTerm Submission: 07/05/2024 (Modules 1 and 2 (for TS classification non DL-based models)) +    - MidTerm Submission: 07/05/2026 
-    - Final Submission: one week before the oral exam (complete project required, also with DL-based models for TS classification). +    - Final Submission: one week before the oral exam (complete project required). 
-    - Dataset: [[https://unipiit-my.sharepoint.com/:u:/g/personal/a_fedele7_studenti_unipi_it/EUSyNv8ahD9FrBZ6fiF3gvABcYVLpbo1biIyOGy8AmcO5g?e=ziQtEc|STD]]+    - Dataset: TBD
  
 ** DM2 Project Guidelines ** ** DM2 Project Guidelines **
-See {{ :dm:dm2_project_guidelines_23_24.pdf | Project Guidelines}}.+See TBD.
  
  
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 ====== Previous years ===== ====== Previous years =====
 +  * [[dm_ds2024-25]]
 +  * [[dm_ds2023-24]]
   * [[dm.2022-23ds]]   * [[dm.2022-23ds]]
   * [[dm.2021-22ds]]   * [[dm.2021-22ds]]
dm/start.1723120692.txt.gz · Ultima modifica: 08/08/2024 alle 12:38 (16 mesi fa) da Salvatore Ruggieri
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