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dm:start [17/12/2025 alle 14:58 (7 settimane fa)] – [Exam Booking Periods] Riccardo Guidottidm:start [05/02/2026 alle 08:20 (3 giorni fa)] (versione attuale) – [Exams] Riccardo Guidotti
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 |04.| 13.10.2025 | 09-11 | E | Data Understanding | {{ :dm:01_dm1_data_understanding_2025_26.pdf | Data Understanding }} | Pedreschi | |04.| 13.10.2025 | 09-11 | E | Data Understanding | {{ :dm:01_dm1_data_understanding_2025_26.pdf | Data Understanding }} | 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 | |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 |
-|04.| 16.10.2025 | 09-11 | E | Data Understanding Lab| {{ :dm:16.10.25_data_understanding_2025_lecture_in_class.zip |}} | Guidotti, Cascione | +|06.| 16.10.2025 | 09-11 | E | Data Understanding Lab| {{ :dm:16.10.25_data_understanding_2025_lecture_in_class.zip |}} | Guidotti, Cascione | 
-|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 | +|07.| 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 | 
-|07.| 23.10.2025 | 09-11 | E | Centroid-based Clustering Algorithm | {{ :dm:05_dm1_kmeans_2025_26.pdf | Centroid-based Clustering}} | Guidotti | +|08.| 23.10.2025 | 09-11 | E | Centroid-based Clustering Algorithm | {{ :dm:05_dm1_kmeans_2025_26.pdf | Centroid-based Clustering}} | Guidotti | 
-|08.| 27.10.2025 | 09-11 | E | Hierarchical Clustering Algorithm | {{ :dm:06_dm1_hierarchical_clustering_2025_26.pdf | Hierarchical Clustering}} | Guidotti | +|09.| 27.10.2025 | 09-11 | E | Hierarchical Clustering Algorithm | {{ :dm:06_dm1_hierarchical_clustering_2025_26.pdf | Hierarchical Clustering}} | Guidotti | 
-|09.| 27.10.2025 | 09-11 | E | Density-based Clustering Algorithm | {{ :dm:07_dm1_density_based_2025_26.pdf | Density-based Clustering}} | Guidotti | +|10.| 27.10.2025 | 09-11 | E | Density-based Clustering Algorithm | {{ :dm:07_dm1_density_based_2025_26.pdf | Density-based Clustering}} | Guidotti | 
-|10.|03.11.2025 | 09-11 | E | Clustering Lab | {{ :dm:03.11.25_clustering_2025_lecture_in_class.zip |}} | Pedreschi, Cascione | +|11.|03.11.2025 | 09-11 | E | Clustering Lab | {{ :dm:03.11.25_clustering_2025_lecture_in_class.zip |}} | Pedreschi, Cascione | 
-|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 | +|12.|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 | 
-|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 | +|13.|06.11.2025 | 09-11 | E | Classification: Naive Bayes Classifier and Exercises | {{ :dm:10_dm1_naive_bayes_2024_25.pptx.pdf | Naive Bayes }} | Pedreschi | 
-|13.|10.11.2025 | 09-11 | E | Classification: Evaluation | {{ :dm:11_dm1_classification_eval_2024_25.pptx.pdf | Model evaluation }} | Pedreschi | +|14.|10.11.2025 | 09-11 | E | Classification: Evaluation | {{ :dm:11_dm1_classification_eval_2024_25.pptx.pdf | Model evaluation }} | Pedreschi | 
-|14.|13.11.2025 | 09-11 | E | Classification: Decision Trees (1) | {{ :dm:12_dm1_decision_trees_2024_25.pptx.pdf | Decision trees }} | Pedreschi | +|15.|13.11.2025 | 09-11 | E | Classification: Decision Trees (1) | {{ :dm:12_dm1_decision_trees_2024_25.pptx.pdf | Decision trees }} | Pedreschi | 
-|15.|17.11.2025 | 09-11 | D5 | Classification: Decision Trees (2) |  | Pedreschi | +|16.|17.11.2025 | 09-11 | D5 | Classification: Decision Trees (2) |  | Pedreschi | 
-|16.|18.11.2025 | 09-11 | C1 | Classification: Decision Trees (3) |  | Pedreschi | +|17.|18.11.2025 | 09-11 | C1 | Classification: Decision Trees (3) |  | Pedreschi | 
-|17.|20.11.2025 | 09-11 | N1 | Classification Lab | {{ :dm:20.11.25_classification_2025_lecture_in_class.zip |}} | Guidotti, Cascione | +|18.|20.11.2025 | 09-11 | N1 | Classification Lab | {{ :dm:20.11.25_classification_2025_lecture_in_class.zip |}} | Guidotti, Cascione | 
-|18.|24.11.2025 | 09-11 | E | Pattern Mining: Apriori | {{ :dm:14_dm1_pattern_mining_2024_25.pptx.pdf | Pattern mining & association rules }} | Pedreschi | +|19.|24.11.2025 | 09-11 | E | Pattern Mining: Apriori | {{ :dm:14_dm1_pattern_mining_2024_25.pptx.pdf | Pattern mining & association rules }} | Pedreschi | 
-|19.|25.11.2025 | 09-11 | C | Pattern Mining: Lift, Interest, Multiattribute |  | Pedreschi | +|20.|25.11.2025 | 09-11 | C | Pattern Mining: Lift, Interest, Multiattribute |  | 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 | +|21.|27.11.2025 | 09-11 | E | Regression: Problem, Linear, KNN, Decision Tree | {{ :dm:13_dm1_linear_regression_2024_25.pptx.pdf | Regression }} | Pedreschi | 
-|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 |+|22.|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 | 
 +|23.|04.12.2025 | 09-11 | C | Exercises Pattern Mining & Decision Trees |  | Guidotti | 
 +|24.|09.12.2025 | 09-11 | M1 | Rule-based Classifiers |{{ :dm:15_dm1_rule_based_classifier_2025_26.pdf | Rule-Based Classifier}}  | Guidotti |
  
  
Linea 201: Linea 203:
 various orals. The dates and slots to take the exam will be published on the course page by the end of various orals. The dates and slots to take the exam will be published on the course page by the end of
 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 respect of the project groups in order to parallelize any discussion on the project. It is not mandatory to take the oral exam together with the other members of the group.  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 respect of the project groups in order to parallelize any discussion on the project. It is not mandatory to take the oral exam together with the other members of the group. 
-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.+In the event that the oral exam is not passed, it will not be possible to take until the next exam session. 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.
  
 ** What: **  ** What: ** 
 The oral test will evaluate the practical understanding of the algorithms. The exam will evaluate three aspects. The oral test will evaluate the practical understanding of the algorithms. The exam will evaluate three aspects.
   - 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 student will have to use pen and paper to show how the exercise is solved.
   - Discussion of the project with questions from the teacher regarding unclear aspects, questionable steps or choices.   - Discussion of the project with questions from the teacher regarding unclear aspects, 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 Enrollment Instruction ===== ===== Exam Enrollment Instruction =====
   * If you are a student of Data Science 1st year    * If you are a student of Data Science 1st year 
-  * Then register here: [[https://forms.gle/598HEN1BMQDzPmiP8|here]]+  * Then register here: [[https://forms.gle/NceAgxW3FmqfSKhu7|here]]
   * Else (not Data Science first year or other degrees like Digital Humanities or any other) register [[https://esami.unipi.it/|here]]   * Else (not Data Science first year or other degrees like Digital Humanities or any other) register [[https://esami.unipi.it/|here]]
-  * Deadline: 09/01/2026 +  * Deadline: 01/02/2026 
-  * Oral Exams will start from the 14/01/2026 +  * Oral Exams will start from the 05/02/2026 
-  * The 10/01/2026 all those registered will receive an email with a link to an Agenda to select the exam day and the time slot+  * Some days after the 01/02/2026 and before the 05/02/2026 all those registered will receive an email with a link to an Agenda to select the exam day and the time slot.
    
  
dm/start.1765983495.txt.gz · Ultima modifica: 17/12/2025 alle 14:58 (7 settimane fa) da Riccardo Guidotti
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