Differenze

Queste sono le differenze tra la revisione selezionata e la versione attuale della pagina.

--- digitalhealth:0001a [14/09/2026 alle 15:21 (29 ore fa)] – [Learning Goals] Anna Monreale
+++ digitalhealth:0001a [14/09/2026 alle 15:55 (28 ore fa)] (versione attuale) – [Exams] Anna Monreale
@@ Linea 19: / Linea 19: @@
 ^  Day of Week  ^  Hour  ^  Room  ^
 |  Monday   |   09:00 - 11:00  |  Room FIB PS4  |
-|  Tuesday  |  14:00 - 16:00    |  Room C1  |
+|  Wensday  |  14:00 - 16:00    |  Room 1-lab  |
-|  Friday   |  11:00 - 13:00 |  Room FIB PS4  |
+|  Friday   |  11:00 - 13:00 |  Room FIB M1  |
 **Office hours - Ricevimento:**
-Anna Monreale: TBD - Online using Teams or in my Office (Appointment by email).
+Riccardo Guidotti:  Online using Teams or in my Office (Appointment by email).
-Francesca Naretto: TBD  - Online using Teams or in my Office (Appointment by email).
+Francesca Naretto:  Online using Teams or in my Office (Appointment by email).
-A [[https://teams.microsoft.com/l/team/19%3AaixkwjuGSoUvrBNsO88NiDZsr8C2yIucNEonmj8ssSY1%40thread.tacv2/conversations?groupId=bfaf6e19-deca-4d53-921c-65b44db73608&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.
+A [[https://teams.microsoft.com/l/team/19%3AyzceX58qk27Y_tUzeiKLC5uGTBYu1KSWlgzTqQkiJV81%40thread.tacv2/conversations?groupId=544aa49e-88f6-48ef-a78c-0f4b94dd4060&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.
 ====== Learning Material -- Materiale didattico ======
@@ Linea 46: / Linea 46: @@
 ===== Past Excercises and past exams of similar courses  =====
+For those of you that opt for the written exam, these are some exercises similar to the practical part of the exam (written exam is composed of practical and theoretical questions).
   * Exercises on Clustering: {{ :dm:ex._clustering.pdf |}}
   * Excercise for DT learning simulation: {{ :magistraleinformatica:dmi:dt-learning-simulation.pdf |}} {{ :magistraleinformatica:dmi:learnedtree.pdf |}}
@@ Linea 54: / Linea 55: @@
 ===== Software=====
+For following the practical lessons of the course, as well as for the project, you need the following softwares:
   * Python - Anaconda (at least 3.7 version!!!): Anaconda is the leading open data science platform powered by Python. [[https://www.anaconda.com/distribution/| Download page]] (the following libraries are already included)
   * Scikit-learn: python library with tools for data mining and data analysis [[http://scikit-learn.org/stable/ | Documentation page]]
@@ Linea 60: / Linea 61: @@
-====== Class Calendar (2025/2026) ======
+====== Class Calendar (2026/2027) ======
-c
 ===== First Semester  =====
 ^ ^ Day ^ Topic ^ Learning material ^ References  ^ Teacher ^
-|  |  22.09  | Strike | | | |
+| |16/09/2026 | Introduction | | | Naretto |
-|  |  23.09  | CANCELED for Teacher's health issues | | | |
+| |18/09/2026 |
-|1.  |  26.09  | Overview. Introduction to Data Analyics for DH  + Data Types | {{ :digitalhealth:0-overview-2025.pdf | Overview}} {{ :digitalhealth:1-intro-da-dm-tecs.pdf |}}|Chap. 1 Kumar Book |Monreale |
+| |21/09/2026 |
-|2.  |  29.09  | Data Understanding TD | {{ :digitalhealth:2-Data_Understanding.pdf | Data Understanding}}|Chap.2 Kumar Book and additioanl resource of Kumar Book: [[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Data Exploration Chap.]] If you have the first ed. of KUMAR this is the Chap 3  |Naretto |
+| |23/08/2026 |
-|3.  |  30.09  | Data Preparation TD | {{ :digitalhealth:3-data_preparation_dad.pdf | Data Preparation}} | Chap.2 Kumar Book and additional resource of Kumar Book: [[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Data Exploration Chap.]] If you have the first ed. of KUMAR this is the Chap 3 | Monreale|
+| |25/09/2026 |
-|4.  |  01.10 - Room I | Python Lab: Data Understanding & Preparation TD | | | Naretto|
+====== Exams ======
-|  |  03.10  | Strike | | | Naretto|
-|5.  |  06.10  | Project Presentation + Data Understanding and Preparation for TD  |{{ :digitalhealth:PIMA-DU.zip | Zip file for DU e DP for TD}} {{ :digitalhealth:Integrazione.zip | Zip file for Python}}| | Naretto|
-|6.  |  07.10  | Clustering: intro and k-means |{{ :digitalhealth:6-basic_cluster_analysis-intro.pdf | Intro clustering}} {{ :digitalhealth:6-basic_cluster_analysis-kmeans.pdf | kmeans}}| Chapter 7, Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar	| Naretto|
-|7.  |  08.10 Room I | Clustering: hierarchical and db-scan  | {{ :digitalhealth:7.basic_cluster_analysis-hierarchical.pdf | hierarchical}} {{:digitalhealth:10-basic_cluster_analysis-dbscan.pdf | DB-scan}}| Chapter 7, Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar	| Naretto|
-|     | 10.10 | suspension of teaching activities | | | |
-|8.   | 13.10 | Density-based clusering + Clustering Validity | {{ :digitalhealth:12-basic_cluster_analysis-validity.pdf |}}| Chapter 7, Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar	 |Naretto |
-|     | 14.10 | Canceled: No Lecture| | | |
-|9.   | 17.10 | Clustering Validity + Data Warehouse | {{ :digitalhealth:6-dw.pdf |}} | | Monreale |
-|10.  | 20.10 | Data Warehouse| {{ :digitalhealth:6-dw.pdf |}}|   | Monreale|
-|11.  | 21.10 | Data Warehouse + PowerBI Demo|Same Slides of the previous lecture | | Monreale|
-|12.  | 22.10 Room Lab I| Pre-processing for Image | {{ :digitalhealth:2.1-data-understanding_images.pdf |}}| | Naretto|
-|13.  | 24.10 |Python Lab: Clustering | {{ :digitalhealth:clustering_diabetes.zip}} | |Naretto |
-|14.  | 27.10 | Pre-processing for Image | {{ :digitalhealth:2.1-data-understanding_images.pdf |}} {{ :digitalhealth:kernel1.pdf |}} |Digital Image processing (Gonzales, Woods) | Naretto|
-|15.  | 28.10 |Time series pre-processing | {{ :digitalhealth:5-data-understanding_ts.pdf|}}| |Monreale |
-|16.  | 31.10 |Time series pre-processing, similarities and project presentation (task n.2) | {{ :digitalhealth:8_time_series_similarity_2024.pdf |}} {{digitalhealth:timeseries_similarity_clustering.zip}}| |Naretto |
-|17.  | 03.11 |Image clustering and presentation of the project (task 3)| {{ :digitalhealth:3.2-clustering_images.pdf |}} {{ :digitalhealth:ecg-first-analysis.ipynb.zip}}| |Naretto |
-|18.  | 04.11 |Image clustering |{{ :digitalhealth:3.2-clustering_images.pdf |}} {{digitalhealth:images_similarity.zip}}| |Naretto |
-|19.  | 07.11 |Project work |  | |Monreale, Naretto |
-|20.  | 10.11 |KNN and Logistic regression  | {{ :digitalhealth:10-KNN.pdf |}} {{ :digitalhealth:10-lg2025.pdf |}} | |Naretto |
-|21.  | 11.11 |LG  |{{ :digitalhealth:10-lg2025.pdf |}} {{digitalhealth:classification-dt-2023.pdf}} | |Naretto |
-|22.  | 14.11 |Rule-based classifier |{{ :digitalhealth:10-Rule-Based-Classifiers.pdf |}} | |Naretto |
-|23.  | 17.11 | Naive Bayes | {{:digitalhealth:11_2021-naive_bayes.pdf}}| |Naretto
-|24.  | 18.11 | Naive Bayes and Ensemble methods| {{:digitalhealth:11_2021-naive_bayes.pdf}} {{:digitalhealth:13_ensemble_2023.pdf}}| |Naretto |
-|25.  | 21.11 | Ensemble methods | {{:digitalhealth:13_ensemble_2023.pdf}}| |Naretto |
-|26.  | 24.11 |Imbalanced learning  | {{:digitalhealth:imbalanced-learning.pdf}}| | |
-|27.  | 25.11 | Python Lab on classification and presentation of the project, task 4|{{:digitalhealth:classification-diabetes.ipynb.zip}}{{:digitalhealth:imbalanced-classification.zip}} | |Naretto |
-|28.  | 28.11 |GSP and Apriori |{{:digitalhealth:18_sequential_patterns_2024.pdf}} | |Monreale |
-|29.  | 01.12 |GSP and Apriori |{{:digitalhealth:17_association_analysis.pdf}} | |Monreale |
-|30.  | 02.12 |Time series |{{:digitalhealth:23_time_series_motif-2024.pdf}}{{:digitalhealth:matrixprofile.pdf}} {{ :digitalhealth:shaplets.pdf |}}| |Monreale |
-|31.  | 05.12 |Time series lab |{{:digitalhealth:23_time_series_motif-2024.pdf}} | |Naretto |
-|32.  | 09.12 |Anomaly detection tabular data |{{:digitalhealth:21_anomaly_detection_2020.pdf}} | |Naretto |
-|  | 12.12 |Strike | | | |
-|33.  | 15.12 |Anomaly detection ts + Python lab |{{:digitalhealth:21_anomaly_detection_2020.pdf}} {{digitalhealth:anomalydetection-1.ipynb.zip}} | | Naretto |
-|34.  | 16.12 |Linear regression and forecasting |{{:digitalhealth:16_linear_regression_2020.pdf}} {{:digitalhealth:ts_forecasting_2025.pdf}} | |Naretto |
-|35.  | 19.12 | Project CHECK - mandatory | | | |
+The course offers **two alternative examination paths**: a project path, for students attending the course, and a written-exam path, for students who do not follow the lessons.
-====== Exams ======
+===== Option 1 – Attending Students: Project + Oral Exam =====
-The exam consists of: a **group project** (in teams of two or three) and an **oral exam** that includes a discussion of the project and an assessment of the theoretical knowledge acquired, for those who complete the project during the course and meet all intermediate and final deadlines set by the instructors.
+Students following the course as attending students are expected to participate in the project activities throughout the semester. The project must be carried out in **groups of 2 or 3 students** and implemented in **Python**.
+During the lectures, the topics and methods needed to carry out the project will be presented. The project must be developed throughout the first semester, following the progress of the course. **Intermediate checkpoints** will be scheduled during the semester. At these checkpoints, each group will be asked to present and discuss the work completed and the results obtained so far. Participation during the lessons, as well as in the intermediate checkpoints and compliance with the corresponding deadlines are part of the project-based examination path.
+The final evaluation consists of:
+  * **Group project: 70% of the final grade**
+  * **Oral exam: 30% of the final grade**
+The project requires the application of the data mining methods presented during the course to the assigned data and research question. The results must be documented in a final report and accompanied by well-commented and executable Python notebooks.
+The oral exam will include questions covering the **entire course programme**. Questions may address both theoretical concepts and practical aspects of the methods presented during the course. The oral exam will also include a discussion of the project and of the methodological choices made by the group.
+The **final project deadline is January 6th, 2027**.
-Alternatively, students who do not complete or submit the project within the established deadlines will be required to take a **written exam** and an **oral exam** covering all course topics.
+Students who choose this examination path must complete and submit the project by this deadline. **Students who do not submit the project by the deadline will no longer be eligible for the project-based examination path and will be required to take the written and oral exams described in Option 2.**
+===== Option 2 – Non-Attending Students: Written + Oral Exam =====
-**PROJECT**
+This examination path applies to students who do not follow the project-based activities during the course **and to students who do not complete or submit the project by the established deadline**.
-A project consists in data analyses based on the use of data mining tools.
+The final evaluation consists of:
-The project has to be performed by a team of 2 max 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.
+  * **Written exam: 70% of the final grade**
+  * **Oral exam: 30% of the final grade**
-Deadline. January 5th, 2026.
+The written exam will cover the **entire course programme** and may include both theoretical questions and practical exercises related to the data mining methods presented during the course.
-Delivery instructions. The final deadline of the project is 5th January 2026 at 23:59. This deadline is STRICT. No extension is possible because then the winter session of exams starts. Groups that will not deliver the project by 5th January will need to do the written exam during the exam sessions. Each group must deliver by email to anna.monreale@unipi.it, francesca.naretto@unipi.it a zipped folder named DM_GroupID.zip and containing 4 folders and 1 pdf file: a folder named DM_GroupID_TASK_DU, containing source code of data understanding; a folder named DM_GroupID_TASK_CLU, containing source code of data clustering; a folder named DM_GroupID_TASK_CLA, containing source code of classification; a folder named DM_GroupID_TASK_TS, containing source code of time series analysis; a pdf file with maximum 25 pages including figures discussing the results of the tasks. The name of this file must be: DM_Report_GroupID.pdf. The file must contain the list of authors (i.e., members of the group). The subject of the email must be “DADHProject25_GroupID”
+The oral exam will also cover the **entire course programme** and may include both theoretical and practical questions.
 ====== Previous years =====
 [[DAD 2024-2025]]