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--- magistraleinformatica:dmi:start [30/09/2021 alle 23:12 (3 anni fa)] – [First Semester] Anna Monreale
+++ magistraleinformatica:dmi:start [15/10/2024 alle 08:35 (4 giorni fa)] (versione attuale) – added second outlier lecture Mattia Setzu
@@ Linea 1: / Linea 1: @@
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+====== Data Mining (309AA) - 9 CFU A.Y. 2024/2025 ======
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-====== Data Mining (309AA) - 9 CFU A.Y. 2021/2022 ======
-**Instructor:**
   * **Anna Monreale**
     * KDDLab, Università di Pisa
     * [[anna.monreale@unipi.it]]
+  * **Mattia Setzu**
+    * KDDLab, Università di Pisa
+    * [[mattia.setzu@unipi.it]]
 **Teaching Assistant:**
-  * **Francesca Naretto**
+  * * **Lorenzo Mannocci**
-    * KDDLab, SNS, Pisa
+    * University of Pisa
-    * [[francesca.naretto@sns.it]]
+    * [[lorenzo.mannocci@phd.unipi.it]]
 ====== News ======
-  * [23.09.2021] ** Please, fill this document: [[https://docs.google.com/spreadsheets/d/1YzHs_JSYPWYqnmkM7ccQc1WZSzGP7UsgxdBF-h5LcEA/edit?usp=sharing|Student-Lists anf Project groups]]. On Teams you can find instructions for GroupID **
+  * [03.10.2024] ** Uploaded project dataset and instructions**
-  * [06.09.2021] The first lecture of this course will take place on Thursday, 16 Sept 2021.
+  * [27.09.2024] ** The Github repo is now live**
-  * [08.09.2021]People that intend to attend the course online should use this link: https://teams.microsoft.com/l/team/19%3aWKvq4kg0XbKZ5pEeiZcarbBXPCYsTvTwMkKZs2PWiHA1%40thread.tacv2/conversations?groupId=aea1385b-6721-4d90-a169-c97f7d066eca&tenantId=c7456b31-a220-47f5-be52-473828670aa1
+  * [21.09.2024] ** Schedule updated, see details below**
+  * [14.09.2024] ** The lectures will start on 19th September 2024**
 ====== Learning Goals ======
      * Fundamental concepts of data knowledge and discovery.
@@ Linea 80: / Linea 25: @@
      * Data preparation
      * Clustering
-     * Classification & Regression
+     * Classification
      * Pattern Mining and Association Rules
      * Outlier Detection
@@ Linea 87: / Linea 32: @@
      * Ethical Issues
-====== Hours and Rooms ======
+====== Schedule ======
 **Classes**
 ^  Day of Week  ^  Hour  ^  Room  ^
-|  Wednesday |  14:00 - 16:00  |  Room C  - Online  |
+|  Tuesday   |  11:00 - 13:00  |  Room C1  |
-|  Thursday  |  14:00 - 16:00  |  Room C  - Online  |
+|  Thursday  |  14:00 - 16:00  |  Room A1  |
-|  Friday    |  09:00 - 11:00  |  Room A1 -  Online  |
+|  Friday    |  09:00 - 11:00  |  Room C1  |
 **Office hours - Ricevimento:**
-Anna Monreale: Wednesday: 11:00-13:00 online using Teams (Appointment by email)
+  * Anna Monreale: Thu 09:00-11:00 - Online using Teams or in my Office (Appointment by email).
-Francesca Naretto: Monday: 15:00-18:00 online using Teams (Appointment by email)
+  * Mattia Setzu: Infos on [[https://unimap.unipi.it/cercapersone/dettaglio.php?ri=177323&template=dett_didattica.tpl|Unimap]]
+A [[https://teams.microsoft.com/l/team/19%3Aq8IK5DrzMwEE5TxVhuw4QdYEVFJ06KVITI5jSJTmaJ81%40thread.tacv2/conversations?groupId=5fae2fa6-38fd-414f-a0c9-ffbd8e6f0710&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, but recordings of the lecture or of the previous years will be made available here for non-attending students.
-====== Learning Material -- Materiale didattico ======
-===== Textbook -- Libro di Testo =====
+====== Teaching Material ======
-  * Pang-Ning Tan, Michael Steinbach, Vipin Kumar. **Introduction to Data Mining**. Addison Wesley, ISBN 0-321-32136-7, 2006
+**Books**
-    * [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php]]
+^ Title ^ Authors ^ Edition ^
-    * Chapters 4,6 and 8 are also available at the publisher's Web site.
+| [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php|Introduction to Data Mining]] | Pang-Ning Tan, Michael Steinbach, Vipin Kumar | 2nd |
-  * Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. **GUIDE TO INTELLIGENT DATA ANALYSIS.** Springer Verlag, 1st Edition., 2010. ISBN 978-1-84882-259-7
+| [[https://link.springer.com/book/10.1007/978-3-031-48956-3|Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications]] | Laura Igual,  Santi Seguí | 2nd |
-  * Laura Igual et al.** Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications**. 1st ed. 2017 Edition.
+| [[http://shop.oreilly.com/product/0636920034919.do| Python Data Science Handbook: Essential Tools for Working with Data]] | Jake VanderPlas | 1st |
-  *  Jake VanderPlas. **[[http://shop.oreilly.com/product/0636920034919.do| Python Data Science Handbook: Essential Tools for Working with Data.]]** 1st Edition.
+| [[https://github.com/janishar/mit-deep-learning-book-pdf|Deep Learning]] | Ian Goodfellow, Yoshua Bengio, Aaron Courville | |
-  *  For Python Notions: {{ :magistraleinformatica:dmi:python_basics.ipynb.zip | Very basic notions on Python}}
+| [[https://math.mit.edu/~gs/linearalgebra/ila5/indexila5.html|Introduction to Linear Algebra]] | Gilbert Strang | 5th |
-===== Slides =====
+**Online tutorials**
+^ ^ Authors ^
+| [[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 |
+**Slides**
+The slides used in the course will be inserted in the calendar after each class. Some are part of the slides provided by the textbook's authors [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php#item4|Slides per "Introduction to Data Mining"]].
-  * The slides used in the course will be inserted in the calendar after each class. Most of them are part of the slides provided by the textbook's authors [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php#item4|Slides per "Introduction to Data Mining"]].
-===== Software=====
+**Software**
-  * Python - Anaconda (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)
+Software material available in the [[https://github.com/data-mining-UniPI/teaching24|Github repository]].
-  * Scikit-learn: python library with tools for data mining and data analysis [[http://scikit-learn.org/stable/ | Documentation page]]
-  * Pandas: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. [[http://pandas.pydata.org/ | Documentation page]]
-====== Class Calendar (2021/2020) ======
+====== Class Calendar (2024/2025) ======
 ===== First Semester  =====
-^ ^ Day ^ Topic ^ Learning material ^ References ^ Video Lectures ^
+^ ^ Day ^ Topic ^ Teaching material ^ References ^ Video Lectures ^ Teacher ^
-|  |  15.09  14:15‑16:00 | Lecture deleted  | | | |
+|    |  17.09  | Candeled   |  |   | |
-|1.|  16.09  14:15‑16:00 | Overview. Introduction to KDD  | {{ :magistraleinformatica:dmi:2021-1-overview.pdf |}}{{ :magistraleinformatica:dmi:1-intro-dm.pdf |}} | Chap. 1 Kumar Book| [[https://unipiit.sharepoint.com/sites/a__td_50479/Shared%20Documents/General/Recordings/Meeting%20in%20_General_-20210916_140839-Meeting%20Recording.mp4?web=1|Video 1]]  [[ https://unipiit.sharepoint.com/sites/a__td_50479/Shared%20Documents/General/Recordings/Meeting%20in%20_General_-20210916_151538-Meeting%20Recording.mp4?web=1|Video 2]] |
+|1.  |  19.09  | Overview. Introduction to KDD   | {{ :magistraleinformatica:dmi:1-overview-2024.pdf |}} {{ :magistraleinformatica:dmi:1-intro-dm-2024.pdf |}}  |Chap. 1 Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_62949/EYl0J0Cq1bNHs38C6SrwncgBxCJWh9U4R5KT6mc5yMRs3g?e=PCkgkD|Part1]] [[https://unipiit.sharepoint.com/:v:/s/a__td_62949/EWFjzWMH_nBHpYeoNtNcC8kByd1bl3WSkjp9Gd4jsxgNVQ?e=bQeAQh|Part2]]| Monreale|
-|2.|  17.09   09:00-10:45 | Data Understanding | {{ :magistraleinformatica:dmi:2-data_understanding.pdf | Slides DU}} |Chap.2 Kumar Book and additioanl resource of Kumar Book:[[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Exploring Data]] If you have the first ed. of KUMAR this is the Chap 3 | [[ https://unipiit.sharepoint.com/sites/a__td_50479/Shared%20Documents/General/Recordings/Data%20Mining%20Lecture-20210917_071017-Meeting%20Recording.mp4|Video 1]]  [[ https://unipiit.sharepoint.com/sites/a__td_50479/Shared%20Documents/General/Recordings/Data%20Mining%20Lecture-20210917_101809-Meeting%20Recording.mp4|Video 2]] |
+|2.  |  20.09  | Data Understanding + Data Preparation (Aggr., Sampling, Dim. Reduction, Feature Selection, Feature Creation).   |  {{ :magistraleinformatica:dmi:2-data_understanding-2024.pdf |}} {{ :magistraleinformatica:dmi:3-data_preparation-2024.pdf |}}|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 | [[https://unipiit.sharepoint.com/:v:/s/a__td_62949/EfBuUxYrbA9CiqB_6oVCsfkB4Gq2NFRbJ1KjQCdX0o6AeQ?e=2wTqaV|Lecture Recording]] |Monreale|
-|3.|  22.09  14:15-16:00 | Data Understanding + Data Preparation        | {{ :magistraleinformatica:dmi:3-data_preparation.pdf |}} | Chap. 2 Kumar Book | [[https://unipiit.sharepoint.com/sites/a__td_50479/Shared%20Documents/General/Recordings/Data%20Mining%20Lecture-20210922_120312-Meeting%20Recording.mp4|Video]] |
+|3    |  24.09  | Data representation   | Slides: {{ :magistraleinformatica:dmi:Data representation.pdf |}}. | References: Introduction to linear algebra (Sections 1, 3.1, 4.2, 6.1, 6.4, 6.5, 7.3), [[https://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf|t-SNE paper]], [[https://arxiv.org/abs/1802.03426 | UMAP paper (Section 3)]]  | [[https://unipiit.sharepoint.com/:v:/s/a__td_62949/EX02UyriTtFHn1TRskD5Dn8Bddv3L24AX3bP_bdGVJTnXg?e=9jTleh|Lecture Recording]] |Setzu  |
-|4.|  23.09  14:15-16:00 | Data Preparation + Data Similarities.|{{ :magistraleinformatica:dmi:4-data_similarity.pdf |}}       | Data Similarity is in Chap. 2  |
+|4.   |  26.09  | Data Cleaning + Transformations. Python Lab: Data Understanding and Preparation |{{ :magistraleinformatica:dmi:5-data_cleaning_transformation.pdf |Data Cleaning and Transformations }} | | [[https://unipiit.sharepoint.com/:v:/s/a__td_62949/EYfnLBl471NJml8dIpRS6qABH9gSIihctow_8BrFt2PT4g?e=i8nDFh|Part1: Data Cleaning_Trasformations]] [[https://unipiit.sharepoint.com/:v:/s/a__td_62949/EeW1ul3HF6RAq79-OLJJOUMB5D_dL7m9gC8RbjFpYGwbOg?e=6NfOLC|Python Lab]]|Monreale, Mannocci |
-|5.|  24.09  09:00-10:45 | Introduction to Clustering. Center-based clustering: kmeans| {{ :magistraleinformatica:dmi:5-basic_cluster_analysis-intro.pdf |}}  {{ :magistraleinformatica:dmi:6.1-basic_cluster_analysis-kmeans.pdf |}}     | Clustering is in Chap. 7  |
+|5.   |  27.09  | Python Lab: Data Understanding and Preparation + Similarities | [[https://github.com/data-mining-UniPI/teaching24|Github repository]] {{ :magistraleinformatica:dmi:6-data_similarity.pdf |}}| |[[https://unipiit.sharepoint.com/:v:/s/a__td_62949/EYJ__0La271Bi2D4NFN44b4BYEF2b9f2praZvHlpBTtcrw?e=Ea38Xq|PythonLab]] [[https://unipiit.sharepoint.com/:v:/s/a__td_62949/EZ-ObDU8SIZHkyPlbV3b9S0BjyhXjSyc30zrncGKSAGgNA?e=HAK0to|Similarity]]|Monreale, Mannocci |
-|6.|  29.09  14:15-16:00 | Hierarchical clustering       | {{ :magistraleinformatica:dmi:7.basic_cluster_analysis-hierarchical.pdf |}} | Chap. 7 Kumar Book |  |
+|6.   |  01.10  | Introduction to Clustering and Centroid-based clustering |{{ :magistraleinformatica:dmi:6-basic_cluster_analysis-intro.pdf |Introduction to Clustering Analysis}} {{ :magistraleinformatica:dmi:6-basic_cluster_analysis-kmeans.pdf |K-means}} |Chap. 7 Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_62949/ESAOwIabbl5DsfUKuobQ_X0Bw2FNXh4EYfB1awaNUMwFlQ?e=GN9B6S|Lecture Recording]] |Monreale |
-|7.|  30.09  14:15-16:00 | Density based clustering. Clustering validity. Lab. DU | {{ :magistraleinformatica:dmi:8.basic_cluster_analysis-dbscan-validity.pdf |}}   {{ :magistraleinformatica:dmi:du.zip |Notebooks on Data Understanding}}   | Chap. 7 Kumar Book  |
+|7.   |  03.10  | Hierarchical Clustering | {{ :magistraleinformatica:dmi:9-basic_cluster_analysis-hierarchical.pdf |}} |Chap. 7 Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_62949/EYXSdANHxrpKqPRcxXd3SOQBYBOTb7FxEZ_rS7KYdSZojQ?e=qibdE2|Lecture Recording]]|Monreale |
-|8.|  01.10  09:00-10:45 | Python Lab - DU + Clustering|     |   |
+|8.   |  04.10  | Density Based Clustering & Variants of K-means | {{ :magistraleinformatica:dmi:10-basic_cluster_analysis-dbscan.pdf |}} {{ :magistraleinformatica:dmi:11-basic_cluster_analysis-kmeans-variants.pdf |}}| Chap. 7 Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_62949/EfULV1PoxuJIuZR8M1Mhz9wBNFSqCVqDN5-L63y-FdChNQ?e=oiOGvR|Lecture Recording]] |Monreale |
+|9.   |  08.10  | Clustering Validation + Python Lab | {{ :magistraleinformatica:dmi:12-basic_cluster_analysis-validity.pdf |}} See github for the python noteebook on clustering| Chap. 7 Kumar Book | |Monreale, Mannocci|
+|   |  10.10  | Lecture canceled due to UNIPI Orienta | | | |
+|   |  11.10  | Lecture canceled due to UNIPI Orienta | | | |
+|10.   |  15.10  | Outlier detection | {{ magistraleinformatica:dmi:Anomaly detection.pdf| Outlier Detection }} | Sections 1.3.1-4, 2.2 Kumar book| | Setzu |
+|11.   |  17.10  | Outlier detection | {{ magistraleinformatica:dmi:Anomaly detection.pdf| Outlier Detection }} | Sections 3.2-3, 4.2-5 2.2 Kumar book| | Setzu |
 ====== Exams ======
-**Mid-term Project **
+**Project:**
 A project consists in data analyses based on the use of data mining tools.
-The project has to be performed by a team of 2/3 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 20 pages of text including figures. The students must deliver both: paper (single column) and  well commented Python Notebooks.
+The project has to be performed by a team of 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.
+  * First part of the project consists in the **assignments** described here: {{  :magistraleinformatica:dmi:project.pdf  | Project Description}}
+  - **Dataset: {{ :magistraleinformatica:dmi:dataset.tar| Dataset}}**
+  - **Deadline**: the fist part has to be delivered by ** November 19th, 2024 **. The delivery will be through Teams' assignement
+  * Second part of the project consists in the assignment described here:
+     - **Deadline**: Dec 29, 2024
+Students who did not deliver the above project within **Dec 29, 2024** need to ask by email a new project to the teachers. The project that will be assigned will require about 20 days of work and after the delivery it will be discussed during the oral exam.
 ** Paper Presentation (OPTIONAL)**
-Students need to present a research paper (made available by the teacher) during the last week of the course. This presentation is OPTIONAL: Students that decide to do the paper presentation can avoid the oral exam with open questions. They only need to present the project (see next point).
+Students need to present a research paper (made available by the teacher) during the last week of the course. This presentation is OPTIONAL: Students that decide to do the paper presentation can avoid the oral exam with open questions on the entire program. They only need to present the project (see next point) and answer open question only on the topics which will not be covered by the project. The paper presentation can be done by the group or by a single person.
 **Oral Exam**
-  * **Project presentation** (with slides) – 10 minutes: mandatory for all the students
+  * **Project presentation** (with slides) – 15 minutes: mandatory for all the students with question fo understanding the details of any part of the project.
-  * ** Open questions ** on the entire program: optional only for students opting for paper presentation.
+  * ** Open questions on the entire program **: for students who will not opt for paper presentation
+  * ** Open questions on the topics which will not be covered by the project ** only for students opting  for paper presentation.
+  * Group presentations of the project are preferred. If this is impossible please contact me for finding a solution.
+**How to book for the exam colloquium? **
+In https://esami.unipi.it/ you can find the dates for the exam: one for January and one for February. Each student must do the registration on one of the 2 dates. These are not the dates of the colloquium or project delivery but we will use the list of registered students for organizing the exam dates. After that deadline we will share with you a calendar for the oral exam.
-===== Reading About the "Data Scientist" Job =====
-** ... a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data. Hal Varian, Google’s chief economist, predicts that the job of statistician will become the "sexiest" around. Data, he explains, are widely available; what is scarce is the ability to extract wisdom from them. **
+====== Previous years =====
+[[DM-INF 2023-2024]]
-//Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.//
+[[DM-INF 2022-2023]]
-  * Data, data everywhere. The Economist, Feb. 2010 {{:dm:economist--010.pdf|download}}
+[[DM-INF 2021-2022]]
-  * Data scientist: The hot new gig in tech, CNN & Fortune, Sept. 2011 [[http://tech.fortune.cnn.com/2011/09/06/data-scientist-the-hot-new-gig-in-tech/|link]]
-  * Welcome to the yotta world. The Economist, Sept. 2011 {{:dm:economist-2012-dm.pdf|download}}
-  * Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, Sept 2012 [[http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/1|link]]
-  * Il futuro è già scritto in Big Data. Il SOle 24 Ore, Sept 2012 [[http://www.ilsole24ore.com/art/tecnologie/2012-09-21/futuro-scritto-data-155044.shtml?uuid=AbOQCOhG|link]]
-  * Special issue of Crossroads - The ACM Magazine for Students - on Big Data Analytics {{:dm:crossroadsxrds2012fall-dl.pdf|download}}
-  * Peter Sondergaard, Gartner, Says Big Data Creates Big Jobs. Oct 22, 2012: [[https://www.youtube.com/watch?v=mXLy3nkXQVM|YouTube video]]
-  * Towards Effective Decision-Making Through Data Visualization: Six World-Class Enterprises Show The Way. White paper at FusionCharts.com. [[http://www.fusioncharts.com/whitepapers/downloads/Towards-Effective-Decision-Making-Through-Data-Visualization-Six-World-Class-Enterprises-Show-The-Way.pdf|download]]
-====== Previous years =====
 [[DM-INF 2020-2021]]