dm:start
Differenze
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| dm:start [24/08/2026 alle 08:45 (4 settimane fa)] – [DM2 - Data Mining: Advanced Topics and Applications (6 CFU)] Fosca Giannotti | dm:start [11/09/2026 alle 09:50 (10 giorni fa)] (versione attuale) – Riccardo Guidotti | ||
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| - | ====== Data Mining A.A. 2025/26 ====== | + | ====== Data Mining A.A. 2026/27 ====== |
| ===== DM1 - Data Mining: Foundations (6 CFU) ===== | ===== DM1 - Data Mining: Foundations (6 CFU) ===== | ||
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| ====== News ====== | ====== News ====== | ||
| - | * **[04.05.2026] Despite the stop, we can keep the planned lectures for these days if we stay as follows: (a) Tomorrow Tuesday 5/5 we start at 14.00 sharp and we end at 15.30 without break; (b) Thursday 7/5 we start at 11.00 sharp and we end at 12.30 without break (as usual); (c) Next Monday 11/5 we can have regular lecture 9.15-11.00.** | + | * [11.09.2026] Lectures will start on Wednesday 30 September |
| - | * [10.03.2026] The lecture of the09.03.2026 will be recovered Wed 18.03.2026 at 11-13 in Room H. The lecture of the 23.03.2026 will be recovered Wed 25.03.2026 at 11-13 in Room D3* | + | |
| - | * [17.12.2025] DM Exam Registration instruction available in Exam section. | + | |
| - | * [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/ | + | |
| - | * [28.07.2025] Lectures will start on Monday 29 September | + | |
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| ^ Day of Week ^ Hour ^ Room ^ | ^ Day of Week ^ Hour ^ Room ^ | ||
| - | | | + | | |
| - | | Thursday | + | | Thursday |
| **Office hours - Ricevimento: | **Office hours - Ricevimento: | ||
| * Prof. Pedreschi | * Prof. Pedreschi | ||
| - | * Monday 15:00-17:00 or Appointment by email | + | * Appointment by email |
| * Room 318 Dept. of Computer Science or MS Teams | * Room 318 Dept. of Computer Science or MS Teams | ||
| * Prof. Guidotti | * Prof. Guidotti | ||
| - | * Thursday 16:00 - 18:00 or Appointment by email | + | * 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 ^ | ||
| - | | | + | | |
| - | | | + | | |
| **Office Hours - Ricevimento: | **Office Hours - Ricevimento: | ||
| - | * Tuesday 15.00-17.00 or Appointment by email | + | * 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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| * Didactic Data Mining [[http:// | * Didactic Data Mining [[http:// | ||
| - | ====== Class Calendar (2025/2026) ====== | + | ====== Class Calendar (2026/2027) ====== |
| ===== 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.| 29.09.2025 | 09-11 | E | Overview, Introduction | + | |01.| 30.09.2025 | 09-11 | E | Overview, Introduction | | Pedreschi | |
| - | |02.| 02.10.2025 | 09-11 | E | The KDD process | {{ : | + | |
| - | |03.| 06.10.2025 | 09-11 | E | Introduction to Python | {{: | + | |
| - | | | 09.10.2025 | + | |
| - | |04.| 13.10.2025 | 09-11 | E | Data Understanding | {{ : | + | |
| - | |05.| 14.10.2025 | 09-11 | C1 | Data Preparation | {{ : | + | |
| - | |06.| 16.10.2025 | 09-11 | E | Data Understanding Lab| {{ : | + | |
| - | |07.| 20.10.2025 | 09-11 | E | Data Similarity and Introduction to Clustering | {{ : | + | |
| - | |08.| 23.10.2025 | 09-11 | E | Centroid-based Clustering Algorithm | {{ : | + | |
| - | |09.| 27.10.2025 | 09-11 | E | Hierarchical Clustering Algorithm | {{ : | + | |
| - | |10.| 27.10.2025 | 09-11 | E | Density-based Clustering Algorithm | {{ : | + | |
| - | |11.|03.11.2025 | 09-11 | E | Clustering Lab | {{ : | + | |
| - | |12.|04.11.2025 | 09-11 | C1 | Classification: | + | |
| - | |13.|06.11.2025 | 09-11 | E | Classification: | + | |
| - | |14.|10.11.2025 | 09-11 | E | Classification: | + | |
| - | |15.|13.11.2025 | 09-11 | E | Classification: | + | |
| - | |16.|17.11.2025 | 09-11 | D5 | Classification: | + | |
| - | |17.|18.11.2025 | 09-11 | C1 | Classification: | + | |
| - | |18.|20.11.2025 | 09-11 | N1 | Classification Lab | {{ : | + | |
| - | |19.|24.11.2025 | 09-11 | E | Pattern Mining: Apriori | {{ : | + | |
| - | |20.|25.11.2025 | 09-11 | C | Pattern Mining: Lift, Interest, Multiattribute | | Pedreschi | | + | |
| - | |21.|27.11.2025 | 09-11 | E | Regression: Problem, Linear, KNN, Decision Tree | {{ : | + | |
| - | |22.|01.12.2025 | 09-11 | E | Lab on Regression and Pattern Mining; FPGROWTH| {{ : | + | |
| - | |23.|04.12.2025 | 09-11 | C | Exercises Pattern Mining & Decision Trees | | Guidotti | | + | |
| - | |24.|09.12.2025 | 09-11 | M1 | Rule-based Classifiers |{{ : | + | |
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| ^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^ | ^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^ | ||
| - | |01.| 16.02.2026 | 9-11 |C| Overview, Imbalanced Learning | {{ : | + | |
| - | |02.| 19.02.2026 | 11-13 |C| Imbalanced Learning, Dimensionality Reduction | {{ : | + | |
| - | |03.| 23.02.2026 | 9-11 |C| Imbalanced Learning | {{ : | + | |
| - | |04.| 26.02.2026 | 11-13 |C| Outlier Detection | {{ : | + | |
| - | |05.| 02.03.2026 | 9-11 |C| Outlier Detection | {{ : | + | |
| - | |06.| 05.03.2026 | 11-13 |C| Outlier Detection | {{ : | + | |
| - | | | 09.03.2026 | 9-11 |C| Canceled. | | Guidotti| | + | |
| - | |07.| 12.03.2026 | 11-13 |C| Gradient Descent, Maximum Likelihood Estimation, Odds, Log Odds, Logistic Regression Intro | {{ : | + | |
| - | |08.| 16.03.2026 | 9-11 |C| Logistic Regression, (Linear)SVM | {{ : | + | |
| - | |09.| 18.03.2026 | 11-13 |H| (NonLinear)SVM, | + | |
| - | |10.| 19.03.2026 | 11-13 |C| Perceptron, Deep Neural Networks | {{ : | + | |
| - | | | 25.03.2026 | 11-13 |D3| Canceled. | | Guidotti| | + | |
| - | |11.| 25.03.2026 | 11-13 |C| Deep Neural Networks | {{ : | + | |
| - | |12.| 26.03.2026 | 11-13 |C| Ensemble Models: Bagging and Random Forest | {{ : | + | |
| - | |13.| 30.03.2026 | 9-11 |C| Ensemble Models: Boosting | {{ : | + | |
| - | |14.| 09.04.2026 | 11-13 |C| Ensemble Models: Boosting, Introduction to XAI | {{ : | + | |
| - | |15.| 13.04.2026 | 9-11 |C| Explainable Artificial Intelligence | {{ : | + | |
| - | |16.| 16.04.2026 | 11-13 |C| Transactional Clustering | {{ : | + | |
| - | |17.| 20.04.2026 | 9-11 |C| Sequential Pattern Mining | {{ : | + | |
| - | |18.| 23.04.2026 | 11-13 |C| Sequential Pattern Mining | {{ : | + | |
| - | |19.| 27.04.2026 | 9-11 |C| Time Series Analytics Introduction | {{ : | + | |
| - | | | 30.04.2026 | 11-13 |C| No Lecture | | Guidotti| | + | |
| - | |20.| 04.05.2026 | 9-11 |C| Time Series Distances | {{ : | + | |
| - | |21.| 05.05.2026 | 14-16 |C| Time Series Approximation and Clustering | {{ : | + | |
| - | |22.| 07.05.2026 | 11-13 |C| Time Series Motif and Discords | {{ : | + | |
| - | |23.| 11.05.2026 | 9-11 |C| Time Series Classification (part 1) | {{ : | + | |
| - | |24.| 14.05.2026 | 11-13 |C| Time Series Classification (part 2) | {{ : | + | |
| ====== Exams ====== | ====== Exams ====== | ||
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| The dates relating to the start of the three exams are/will be published on the online platform | The dates relating to the start of the three exams are/will be published on the online platform | ||
| https:// | https:// | ||
| - | various orals. The dates and slots to take the exam will be published on the course page by the end of | + | various orals. Each student must register on https:// |
| - | May. Each student must also register on https:// | + | |
| 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. | 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. | ||
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| ===== 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:// | + | * Then register here: [[TODO|here]] |
| * Else (not Data Science first year or other degrees like Digital Humanities or any other) register [[https:// | * Else (not Data Science first year or other degrees like Digital Humanities or any other) register [[https:// | ||
| - | * Deadline: | + | * Deadline: |
| - | * Oral Exams will start from the 05/02/2026 | + | * Oral Exams will start from ??/??/2026 |
| - | * 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. | + | * Some days after the ??/??/2026 and before the ??/??/2026 all those registered will receive an email with a link to an Agenda to select the exam day and the time slot. |
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| * **Dataset** | * **Dataset** | ||
| - | - Assigned: 15/10/2025 | + | - Assigned: 15/10/2026 |
| - MidTerm Submission: 15/11/2025 (+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: 31/12/2025 (+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: Download here {{ : | + | - Dataset: Download here TO UPDATE |
| ** DM1 Project Guidelines ** | ** DM1 Project Guidelines ** | ||
| - | See {{ : | + | See TO UPDATE |
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| * **Dataset** | * **Dataset** | ||
| - | - Assigned: | + | - Assigned: |
| - | - MidTerm Submission: | + | - MidTerm Submission: |
| - Final Submission: one week before the oral exam (complete project required). | - Final Submission: one week before the oral exam (complete project required). | ||
| - | - Dataset: Download Tabular Dataset here: {{ : | + | - Dataset: Download Tabular Dataset here: TO UPDATE |
| - | - Dataset: Download Time-Series Dataset here: {{ : | + | - Dataset: Download Time-Series Dataset here: TO UPDATE |
| ** DM2 Project Guidelines ** | ** DM2 Project Guidelines ** | ||
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| ====== Previous years ===== | ====== Previous years ===== | ||
| + | * [[dm_ds2025-26]] | ||
| * [[dm_ds2024-25]] | * [[dm_ds2024-25]] | ||
| * [[dm_ds2023-24]] | * [[dm_ds2023-24]] | ||
dm/start.1787561148.txt.gz · Ultima modifica: da Fosca Giannotti