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dm:start [05/12/2022 alle 10:59 (16 mesi fa)]
Riccardo Guidotti [First Semester (DM1 - Data Mining: Foundations)]
dm:start [26/03/2024 alle 17:16 (44 ore fa)] (versione attuale)
Riccardo Guidotti [Second Semester (DM2 - Data Mining: Advanced Topics and Applications)]
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-====== Data Mining A.A. 2022/23 ======+====== Data Mining A.A. 2023/24 ======
  
 ===== DM1 - Data Mining: Foundations (6 CFU) ===== ===== DM1 - Data Mining: Foundations (6 CFU) =====
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 Teaching Assistant Teaching Assistant
-  * **Francesco Spinnato** +  * **Andrea Fedele** 
-    * KDDLab, Scuola Normale Superiore +    * KDDLab, Università di Pisa 
-    * [[https://kdd.isti.cnr.it/people/spinnato-francesco]] +    * [[https://www.linkedin.com/in/andrea-fedele/?originalSubdomain=it]] 
-    * [[francesco.spinnato@sns.it]]  +    * [[andrea.fedele@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
-  * **Francesco Spinnato** +  * **Andrea Fedele** 
-    * KDDLab, Scuola Normale Superiore +    * KDDLab, Università di Pisa 
-    * [[https://kdd.isti.cnr.it/people/spinnato-francesco]] +    * [[https://www.linkedin.com/in/andrea-fedele/?originalSubdomain=it]] 
-    * [[francesco.spinnato@sns.it]]  +    * [[andrea.fedele@phd.unipi.it]]   
 +    * Meeting: https://calendly.com/andreafedele/
 ====== News ====== ====== News ======
-     * [15.09.2022] Project Groups [[https://docs.google.com/spreadsheets/d/1j5A6JPurO6o3ycjb4qc1lKZ4K2HqpdQhb_eyII_37dc/edit?usp=sharing|link]] + 
-     * [15.09.2022] MS Teams [[https://teams.microsoft.com/l/team/19%3a-E-BCEQRJk-qyKrkyNoos6n4h6neLOfJM4zI5GxY9Us1%40thread.tacv2/conversations?groupId=dfb4c6f2-9430-4eda-8bb4-69bdebd5e01b&tenantId=c7456b31-a220-47f5-be52-473828670aa1|link]]  +     * **[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. 
-     * [15.09.2022] Lectures will be in presence only. Registrations of the lectures of past years can be found at the bottom of this web page. +     * [13.10.2023] To schedule meeting with the Teaching Assistant you can use: https://calendly.com/andreafedele/ 
-     * **[23.11.2022]** In order to recover from skipped and suspended lectures we signal the presence of two new dates in unusual slots for our lectures, i.e., Wed 7th Dec 14.00-16.00 Room A1 and Wed 14th Dec 14.00-16.00 Room A1.+     * [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  |  Aula A1   |  +|  Monday  |  11:00 - 13:00  |  C1   |  
-|  Thursday   11:00 - 13:00  |  Aula A1  +|  Wednesday   11:00 - 13:00  |  C1  
  
 **Office hours - Ricevimento:** **Office hours - Ricevimento:**
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       * Online       * Online
   * Prof. Guidotti   * Prof. Guidotti
-      * Wednesday 15-17 or Appointment by email+      * Tuesday 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
  
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 ^  Day of Week  ^  Hour  ^  Room  ^  ^  Day of Week  ^  Hour  ^  Room  ^ 
-|  ???  |  11:00 - 13:00  |  ???  |  +|  Monday   |  09:00 - 11:00  |  C   |  
-|  ???   11:00 - 13:00  |  ???  |  +|  Wednesday   11:00 - 13:00  |   |  
  
 **Office Hours - Ricevimento:** **Office Hours - Ricevimento:**
  
-  * Wednesday 15-17 or Appointment by email+  * Tuesday 15.00-17.00 or Appointment by email
   * Room 363 Dept. of Computer Science or MS Teams   * Room 363 Dept. of Computer Science or MS Teams
  
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   * [[http://www.knime.org | KNIME ]] The Konstanz Information Miner. [[http://www.knime.org/download-desktop| Download page ]]   * [[http://www.knime.org | KNIME ]] The Konstanz Information Miner. [[http://www.knime.org/download-desktop| Download page ]]
   * [[http://www.cs.waikato.ac.nz/ml/weka/ | WEKA ]] Data Mining Software in JAVA. University of Waikato, New Zealand [[http://www.cs.waikato.ac.nz/ml/weka/ | Download page ]]   * [[http://www.cs.waikato.ac.nz/ml/weka/ | WEKA ]] Data Mining Software in JAVA. University of Waikato, New Zealand [[http://www.cs.waikato.ac.nz/ml/weka/ | Download page ]]
-  * Didactic Data Mining [[http://matlaspisa.isti.cnr.it:5055/DDM]]+  * Didactic Data Mining [[http://matlaspisa.isti.cnr.it:5055/HelpDDMv1]], [[https://kdd.isti.cnr.it/ddm/#/| DDMv2]] 
    
-====== Class Calendar (2021/2022) ======+====== Class Calendar (2023/2024) ======
  
 ===== First Semester (DM1 - Data Mining: Foundations) ===== ===== First Semester (DM1 - Data Mining: Foundations) =====
  
-^ ^ Day ^ Time ^ Room ^ Topic ^ Learning Material ^ Lecturer ^ +^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^ 
-|01.| 15.09.2022 | 11-13 |A1| Overview, Intro, KDD and CRIPS. | {{ :dm:00_dm1_introduction_2022_23.pdf | Intro}} | Pedreschi/Guidotti +|01.| 18.09.2023 | 11-13 |C1| Overview, Introduction | {{ :dm:00_dm1_introduction_2023_24.pdf | Intro}} | Pedreschi| 
-|   19.09.2022 | 11-13 |  | No Lecture |  |  | +|   20.09.2023 | 11-13 |  | No Lecture |  |  | 
-|02.| 22.09.2022 | 11-13 |A1Project Guideliens & Intro to Python | {{ :dm:dm1_project_guidelines_22_23.pdf | Project Guidelines}}, {{ :dm:dm1_lab01_python_basics.zip | Intro Python}} | Spinnato | +|02.| 25.09.2023 | 11-13 |C1Lab. Introduction to Python | {{ :dm:dm1_lab01_python_basics.zip | Python Basic}} | Guidotti
-|   | 26.09.2022 | 11-13 |  | No Lecture |  |  +|03.| 27.09.2023 | 11-13 |C1Lab. Data Understanding | {{ :dm:dm1_lab02_data_understanding.zip | Data Understanding}} | Guidotti
-|03.| 29.09.2022 | 11-13 |A1| Data Understanding | {{ :dm:01_dm1_data_understanding_2022_23.pdf | Data Understanding}}  Pedreschi +|04.| 02.10.2023 | 11-13 |C1| Data Understanding | {{ :dm:01_dm1_data_understanding_2023_24.pdf | Data Understanding}} | Guidotti
-|04.| 03.10.2022 | 11-13 |A1| Data Understanding & Data Preparation  | {{ :dm:02_dm1_data_preparation_2022_23.pdf | Data Preparation}}| 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
-|05.| 06.10.2022 | 11-13 |A1Lab. Data Understanding | {{ :dm:data_understanding.zip | Data Und Python}} | Spinnato/Guidotti | +|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| 
-|   | 10.10.2022 11-13  | No Lecture |  |  +|07.| 11.10.2023 | 11-13 |C1Data Similarity & Lab. Data Understanding | {{ :dm:03_dm1_data_similarity_2023_24.pdf | Data Similarity}}, {{ :dm:dm1_lab02_data_understanding.zip Data Understanding}} | Pedreschi| 
-|06.| 13.10.2022 | 11-13 |A1| Data PreparationSimilarity | {{ :dm:03_dm1_data_similarity_2022_23.pdf | Data Similarity}}, {{ :dm:data_understanding.zip | Data Und Python}} | Pedreschi | +|08.| 16.10.2023 | 11-13 |C1Introduction to Clustering, K-Means | {{ :dm:04_dm1_clustering_intro_2023_24.pdf | Intro_Clustering}}, {{:dm:05_dm1_kmeans_2023_24.pdf | K-Means }} | Pedreschi| 
-|07.| 17.10.2022 | 11-13 |A1Intro Clustering, K-Means | {{ :dm:04_dm1_clustering_intro_2022_23.pdf | Intro Clustering}}, {{ :dm:05_dm1_kmeans_2022_23.pdf K-Means}} | Pedreschi | +|09.| 18.10.2023 | 11-13 |C1Clustering Validation, Hierarchical Clustering | {{ :dm:04_dm1_clustering_intro_2023_24.pdf | Intro_Clustering}}, {{ :dm:06_dm1_hierarchical_clustering_2023_24.pdf | Hierarchical}} | Pedreschi| 
-|08.| 20.10.2022 | 11-13 |A1| K-Means | {{ :dm:05_dm1_kmeans_2022_23.pdf | K-Means}} | Pedreschi | +|10.| 23.10.2023 | 11-13 |C1Density-based Clustering | {{ :dm:07_dm1_density_based_2023_24.pdf Density-based Clustering}} | Pedreschi
-|09.| 24.10.2022 | 11-13 |A1| Hierarchical & Density-based | {{ :dm:06_dm1_hierarchical_clustering_2022_23.pdf | Hierarchical}}, {{ :dm:07_dm1_density_based_2022_23.pdf | Density}} | Pedreschi | +|11.25.10.2023 | 11-13 |C1Lab. Clustering {{ :dm:dm1_lab03_clustering.zip Clustering}}| Guidotti
-|10.| 27.10.2022 | 11-13 |A1Lab. Clustering | {{ :dm:clustering.zip | Clustering Python}} | Spinnato/Guidotti +|12.| 30.10.2023 | 11-13 |C1Ex. Clustering | {{ :dm:ex1_dm1_clustering_2023_24.pdf | ExClustering}}| Guidotti| 
-  30.10.2022 | 11-13 |  No Lecture   +  | 01.11.2023 11-13 |  | No Lecture |  |  | 
-|11.| 03.11.2022 | 11-13 |A1Exercises Clustering | {{ :dm:ex1_dm1_clustering_2022_23.pdf | Exercises 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| 
-|12.| 07.11.2022 | 11-13 |A1| Intro Classification | {{ :dm:08_dm1_classification_intro_2022_23.pdf | Intro Classification}}, {{ :dm:09_dm1_knn_2022_23.pdf | kNN}} | Guidotti | +|14.| 08.11.2023 | 11-13 |C1Naive Bayes, Exercises | {{ :dm:10_dm1_naive_bayes_2023_24.pdf | Naive Bayes}} | Guidotti| 
-|13.| 10.11.2022 | 11-13 |A1Eval Measures, Exercises kNN | {{ :dm:08_dm1_classification_intro_2022_23.pdf | Intro Classification}}, {{ :dm:09_dm1_knn_2022_23.pdf | kNN}} | Guidotti | +|15.| 13.11.2023 | 11-13 |C1Model Evaluation | {{ :dm:11_dm1_classification_eval_2023_24.pdf | Model Evaluation}} | Guidotti| 
-|14.| 14.11.2022 | 11-13 |A1Decision Tree | {{ :dm:10_dm1_decision_trees_2022_23.pdf | Decision Trees}} | Guidotti | +|16.| 15.11.2023 | 11-13 |C1Model Evaluation Exercises & Lab | {{ :dm:dm1_lab04_classification_regression.zip Classification}} | Guidotti| 
-|15.| 17.11.2022 | 11-13 |A1Decision Tree, Exercises DT | {{ :dm:10_dm1_decision_trees_2022_23.pdf Decision Trees}}, {{ :dm:tree_exercise.xlsx Ex DT}} Guidotti +|   | 20.11.2023 11-13  | No Lecture |  |  
-|16.| 22.11.2022 | 11-13 |A1| Decision Tree | {{ :dm:10_dm1_decision_trees_2022_23.pdf | Decision Trees}} | Guidotti +|17.| 22.11.2023 | 11-13 |C1| Decision Tree Classifier | {{ :dm:12_dm1_decision_trees_2023_24.pdf | Decision Tree}} | Pedreschi
-|17.| 24.11.2022 | 11-13 |A1Naive Bayes Classifier | {{ :dm:11_dm1_naive_bayes_2022_23.pdf | NBC}} | Guidotti +|18.| 27.11.2023 | 11-13 |C1Decision Tree Classifier | {{ :dm:12_dm1_decision_trees_2023_24.pdf | Decision Tree}} | Pedreschi
-|18.| 28.11.2022 | 11-13 |A1| Lab. Classification | {{ :dm:classifcazion.zip | Classification Python}} | Spinnato/Guidotti | +|19.| 29.11.2023 | 11-13 |C1Exercises and Lab. Decision Tree Classifier | {{ :dm:dm1_lab04_classification.zip | Decision Tree}} | Guidotti| 
-|19.| 01.12.2022 | 11-13 |A1Intro Regression | {{ :dm:12_dm1_linear_regression_2022_23.pdf | Intro Regression}} | Guidotti +|20.| 04.12.2023 | 11-13 |C1Decision Tree Classifier, Exercises and Lab | {{ :dm:12_dm1_decision_trees_2023_24.pdf | Decision Tree}} | Pedreschi
-|20.| 05.12.2022 | 11-13 |A1Pattern Mining | {{ :dm:13_dm1_pattern_mining_2022_23.pdf | Pattern Mining}} | Pedreschi +|21.| 06.12.2023 | 11-13 |C1Intro Regression & Lab. Regression | {{ :dm:12_dm1_linear_regression_2023_24.pdf | Regression}}, {{ :dm:dm1_lab05_regression.zip Regression}} | Guidotti
-|21.| 07.12.2022 14-16 |A1| Pattern Mining |  | Pedreschi | +|22.| 11.12.2023 11-13 |C1Into Pattern Mining and Apriori {{ :dm:13_dm1_pattern_mining_2023_24.pdf | Pattern Mining}} | Pedreschi| 
-  | 08.12.2022 11-13 |  | No Lecture |  |  | +|23.| 13.12.2023 16-18 |C1Apriori & Lab. Pattern Mining {{ :dm:13_dm1_pattern_mining_2023_24.pdf Pattern Mining}}, {{ :dm:dm1_lab06_pattern_mining.zip Pattern Mining}}  Pedreschi
-|22.12.12.2022 11-13 |A1TBD  | Guidotti | +|24.| 18.12.2023 | 11-13 |CFP-Growth and Exercises | {{ :dm:13_dm1_pattern_mining_2023_24.pdf | Pattern Mining}} | Guidotti|
-|23.| 14.12.2022 | 14-16 |A1| TBD |  | Guidotti +
-|24.| 15.12.2022 | 11-13 |A1Lab. Pattern Mining |  | Spinnato/Guidotti |+
 ===== Second Semester (DM2 - Data Mining: Advanced Topics and Applications) ===== ===== Second Semester (DM2 - Data Mining: Advanced Topics and Applications) =====
  
-^ ^ Day ^ Room  ^ Topic ^ Learning Material ^ Instructor +^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer 
-| 01.| 14.02.2022 11:00--13:00     | Guidotti | +|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| 
 +|   | 21.02.2024 |  | | No Lecture |  |  | 
 +|   | 26.02.2024 |  | | No Lecture |  |  | 
 +|02.| 19.02.2024 | 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|
 ====== Exams ====== ====== Exams ======
  
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 ** 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 (if online, the student can use the Miro graphic system https://miro.com/ during the explanations)+  - 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,
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 average mark of DM1 and DM2. average mark of DM1 and DM2.
  
-**Exam Booking Periods**+===== Exam Booking Periods =====
   * Exam portal link: [[https://esami.unipi.it/|here]]   * Exam portal link: [[https://esami.unipi.it/|here]]
-  * 1st Appello: 11/12/2022 00:00 - 05/01/2023 23:59 +  * 1st Appello: from 09/01/2024 to 31/12/2024 
-  * 2nd Appello: 01/01/2023 00:00 - 26/01/2023 23:59+  * 2nd Appello: from 01/02/2024 to 17/02/2024 
 +  * 3rd Appello:  
 +  * 4th Appello:  
 +  * 5th Appello:  
 +  * 6th Appello
    
-**Exam Booking Agenda** +===== Exam Booking Agenda ===== 
-  * Agenda Link[[https://agende.unipi.it/nfj-juo-qms|here]] +  * 1st Appello - DM1: https://agende.unipi.it/yra-ief-dmo, DM2: https://agende.unipi.it/rnm-urj-wsu 
-  * 1st Appello: starts 10/01/2023 +  * 2nd Appello - DM1: https://agende.unipi.it/yra-ief-dmo, DM2: https://agende.unipi.it/rnm-urj-wsu 
-  * 2nd Appello: starts 31/01/2023+  * 3rd Appello:  
 +  * 4th Appello:  
 +  * 5th Appello:  
 +  * 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 [[datamining.unipi@gmail.com]]. Please, use “[DM1 2022-2023] 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 [[andrea.fedele@phd.unipi.it]] and [[riccardo.guidotti@unipi.it]]. Please, use “[DM1 2023-2024] Project” in the subject.
    
   * **Dataset**   * **Dataset**
-    - Assigned: 15/09/2021 +    - Assigned: 25/09/2023 
-    - MidTerm Submission: **28/11/2022 (extended)** (half project required, i.e., Data Understanding & Preparation and Clustering) +    - MidTerm Submission: 15/11/2023 (+0.5) (half project required, i.e., Data Understanding & Preparation and Clustering) 
-    - Final Submission: **31/12/2022** or one week before the oral exam (complete project required). +    - Final Submission: 31/12/2023 (+0.5) one week before the oral exam (complete project required). 
-    - Dataset: {{:dm:ravdess_dm1_2223.zip | RAVDESS}} +    - Dataset: {{ :dm:std.zip | STD}}
-    - Link original pages: [[https://zenodo.org/record/1188976#.YyLSI-xBz0o| zenodo]], [[https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio| kaggle1]], [[https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-song-audio| kaggle2]]+
  
 ** DM1 Project Guidelines ** ** DM1 Project Guidelines **
-See {{ :dm:dm1_project_guidelines_22_23.pdf | Project Guidelines}}.+See {{ :dm:dm1_project_guidelines_23_24.pdf | Project Guidelines}}.
  
  
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 ===== Exam DM2 ====== ===== Exam DM2 ======
  
-TBD +The exam is composed of two parts: 
 + 
 +  * 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. 
 +  
 +  * **Dataset** 
 +    - Assigned: 19/02/2024 
 +    - MidTerm Submission: 30/04/2024 (Modules 1 and 2 (for TS classification non DL-based models) 
 +    - Final Submission: one week before the oral exam (complete project required, also with DL-based models for TS classification). 
 +    - Dataset: [[https://unipiit-my.sharepoint.com/:u:/g/personal/a_fedele7_studenti_unipi_it/EUSyNv8ahD9FrBZ6fiF3gvABcYVLpbo1biIyOGy8AmcO5g?e=ziQtEc|STD]] 
 + 
 +** DM2 Project Guidelines ** 
 +See {{ :dm:dm2_project_guidelines_23_24.pdf | Project Guidelines}}. 
  
  
  
-====== Exam Dates ====== 
  
-===== Exam Sessions ===== 
-^ Session ^ Date  ^ Room   ^ Notes ^ Marks ^ 
-|1.|10.01.2023| | Please, use the system for registration: https://esami.unipi.it/ | | 
-|2.|31.01.2023| | Please, use the system for registration: https://esami.unipi.it/ | | 
-|3.|??.??.2023| | Please, use the system for registration: https://esami.unipi.it/ | | 
-|4.|??.??.2023| | Please, use the system for registration: https://esami.unipi.it/ | | 
-|5.|??.??.2023| | Please, use the system for registration: https://esami.unipi.it/ | | 
-|6.|??.??.2023| | Please, use the system for registration: https://esami.unipi.it/ | | 
 ===== Past Exams ===== ===== Past Exams =====
   * Past exams texts can be found in old pages of the course. Please do not consider these exercises as a unique way of testing your knowledge. Exercises can be changed and updated every year and will be published together with the slides of the lectures.   * Past exams texts can be found in old pages of the course. Please do not consider these exercises as a unique way of testing your knowledge. Exercises can be changed and updated every year and will be published together with the slides of the lectures.
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 ====== Previous years ===== ====== Previous years =====
 +  * [[dm.2022-23ds]]
   * [[dm.2021-22ds]]   * [[dm.2021-22ds]]
   * [[dm.2020-21]]   * [[dm.2020-21]]
dm/start.1670237966.txt.gz · Ultima modifica: 05/12/2022 alle 10:59 (16 mesi fa) da Riccardo Guidotti