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--- magistraleinformatica:dmi:start [14/09/2026 alle 12:55 (8 giorni fa)] – Anna Monreale
+++ magistraleinformatica:dmi:start [21/09/2026 alle 10:59 (19 ore fa)] (versione attuale) – [First Semester] Anna Monreale
@@ Linea 12: / Linea 12: @@
 ====== News ======
+**[17-09-2026] Introduction Python Notebook**
+Hi everyone,
+I have uploaded a notebook on Didawiki covering the basics of Python for those who are not familiar with the programming language.
+Lorenzo
+----
+**[15-09-2026] Exam Modality and Project Groups**
+Hi everyone,
+I have created a shared Google Sheet to collect information about your **exam modality** and, for students choosing the Project, to organize the project groups.
+🔗 **Google Sheet**: [[https://docs.google.com/spreadsheets/d/1qNdnhYP-SDP4sEpsreu2V1j-_SlrZRMdyG8jG4zepJA/edit?usp=sharing]]
+⚠️ **All students must fill in the Google Sheet, regardless of whether they choose the Project or the Written Exam.**
+Please fill in the following fields:
+  * **ID**: already pre-filled. Do not modify it.
+  * **Surname, Name, Student ID, Email**: enter your personal information.
+  * **Curriculum**: select your curriculum from the available options.
+  * **Exam's modality**: select one of the two available options:
+  *   * Project
+  *   * Written exam
+  * **Group ID**: fill in this field only if you choose the Project. Groups must consist of 3 students. All members of the same group must enter the same numerical Group ID. When creating a new group, use the next available number (e.g., if the last existing Group ID is 4, the new group should use 5).
+  * **Note**: optional; use it only if you need to communicate additional information.
+📅 **Deadline: September 29**
+By this date, **all students must have completed the Google Sheet and selected their exam modality**. Students choosing the Project must also have formed a group of 3 students and entered the corresponding Group ID.
+You are free to choose your group members. However, we **encourage you to form groups with students from different curricula** whenever possible, as different backgrounds can bring complementary knowledge and skills that can be useful when working on the project.
+Please make sure not to modify information entered by other students.
+----
 ====== Learning Goals ======
 The Data Mining course tackles the analysis of large collections of data, and the extraction of information and patterns. It aims to explore core components of the Knowledge Discovery from Data (KDD) process, and focuses on:
@@ Linea 59: / Linea 99: @@
 | [[https://brianmcfee.net/dstbook-site/content/intro.html|Digital Signals Theory]] | Brian McFee |
 | [[https://rtavenar.github.io/blog/dtw.html|An introduction to Dynamic Time Warping]] | Romain Tavenard |
-| [[https://github.com/msetzu/intro_to_ds_and_ml/blob/master/python/notebooks/Python.ipynb|Introduction to Python]] | Mattia Setzu |
+| {{ :magistraleinformatica:dmi:0_python_intro_for_data_mining.ipynb.zip |}} | Lorenzo Mannocci |
@@ Linea 81: / Linea 121: @@
 ^ ^ Day ^ Topic ^ Teaching material ^ References ^ Teacher ^
-|1.  | 15.09  | Course Overview. Introduction to Data Mining |   | Chap. 1 Kumar Book | Monreale  |
+|1.  | 15.09  | Course Overview. Introduction to Data Mining | {{ :magistraleinformatica:dmi:1-overview-2026.pdf |Course Overview}} {{ :magistraleinformatica:dmi:1-intro-dm-2026.pdf | Intro to DM tasks}}  | Chap. 1 Kumar Book | Monreale  |
-|2.  | 17.09  |          |   |  | |
+|2.  | 17.09  |  Data Understanding        | {{:undefined:2-data_understanding-2026.pdf | Slides}} Please, download again this set of slides!| 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 | |
-|3.  | 21.09  |          |   |  | |
+|3.  | 21.09  |   Data Understanding       |   The same form the previous lecture |  | |
@@ Linea 93: / Linea 133: @@
 **Project track**:
-  * Project (70% of the final score) to be delivered after the end of the course and discussed during the oral exam. Note that the project score will be finalized with the project discussion.
+  * Project to be delivered after the end of the course and discussed during the oral exam. Note that the project score will be finalized with the project discussion.
-  * Oral exam (30% of the final score)
+  * Oral exam
 During the course, you will have some “Project presentation” sessions wherein you’ll briefly (~3 minutes) present your work, and receive feedback from the lecturers. These sessions do not contribute to your grade.
 **Written test track**
-  * Written exam (70% of the final score): to be delivered after the end of the course during the exam sessions and can include both theoretical questions and exercises.
+  * Written exam: to be delivered during the standard exam sessions and can include both theoretical questions and exercises.
-  * Oral exam (30% of the final score)
+  * Oral exam
 Note that a passing grade for the project/written exam is required to be admitted to the oral exam.