|Day of Week||Hour||Room|
|Wednesday||09:00 - 11:00||Room E|
|Thursday||11:00 - 13:00||Room C|
|Friday||09:00 - 11:00||Room C|
Office hours - Ricevimento: Anna Monreale: Tuesday: 11:00-13:00 by online using Teams or at the Department of Computer Science, room 374/E (Please ask an appointment by email). Francesca Naretto: TDB
A 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.
|Day||Topic||Learning material||References||Video Lectures|
|1.||15.09 11:00‑13:00||Overview. Introduction to KDD||1-overview.pdf 1-intro-dm.pdf||Chap. 1 Kumar Book||Video 1: Course Overview;Video 2: Introduction DM (the recording of the Introduction had some audio issue so I published the part of the lecture of the a.y. 2021/22)|
|2.||16.09 09:00-11:00||Data Understanding||2-data_understanding.pdf||Chap.2 Kumar Book and additioanl resource of Kumar Book:Exploring Data If you have the first ed. of KUMAR this is the Chap 3||Video 1: Data Understanding - Part 1; Video 2: Data Understanding - Part 2|
|3.||21.09 09:00-11:00||Data Understanding & Data Preparation||Chap.2 Kumar Book and additioanl resource of Kumar Book:Exploring Data If you have the first ed. of KUMAR this is the Chap 3||Video: Data Understanding & Data Preparation|
|4.||22.09 11:00-13:00||Data Preparation + Data Similarities.||4-data_similarity.pdf||Data Similarity is in Chap. 2||Video 1: Data Preparation + Data Similarities - Part 1; Video 2: Data Preparation + Data Similarities - Part 2|
|5.||23.09 09:00-11:00||Introduction to Clustering. Center-based clustering: kmeans||5-basic_cluster_analysis-intro.pdf 6.1-basic_cluster_analysis-kmeans.pdf||Clustering is in Chap. 7||Video 1: Introduction to Clustering + K-means - Part 1;Video 2: Introduction to Clustering + K-means - Part 2]|
|6.||28.09 09:00-11:00||Python Lab: Data Understanding & Data Preparation||Notebook DU tips||Video 1: Python Lab: DU - Part1;Video 2: Python Lab: DU - Part2|
|7.||29.09 11:00-13:00||Hierarchical clustering||7.basic_cluster_analysis-hierarchical.pdf|
|8.||30.09 09:00-11:00||Lecture Canceled|
A project consists in data analyses based on the use of data mining tools. 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.
Students who did not deliver the above project within DATE TBD need to ask by email a new project to the teachers. The project that will be assigned will require about 2 weeks 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). The paper presentation can be done by the group or by a single person.