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magistraleinformatica:dmi:start

Data Mining (309AA) - 9 CFU A.Y. 2022/2023

Instructor:

Teaching Assistant:

News

  • [09.09.2022] 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 Goals

  • Fundamental concepts of data knowledge and discovery.
  • Data understanding
  • Data preparation
  • Clustering
  • Classification
  • Pattern Mining and Association Rules
  • Outlier Detection
  • Time Series Analysis
  • Sequential Pattern Mining
  • Ethical Issues

Hours and Rooms

Classes

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.

Learning Material -- Materiale didattico

Textbook -- Libro di Testo

Slides

Software

  • Python - Anaconda (at least 3.7 version!!!): Anaconda is the leading open data science platform powered by Python. Download page (the following libraries are already included)
  • Scikit-learn: python library with tools for data mining and data analysis 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. Documentation page

Class Calendar (2022/2023)

First Semester

Day Topic Learning material References Video Lectures
1. 15.09 11:00‑13:00 Overview. Introduction to KDD 1-overview.pdf 1-intro-dm.pdfChap. 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]

Exams

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 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: ….
  1. Dataset: ….
  2. Deadline: the fist part has to be delivered within November, 5th 2022. Send an email to: anna.monreale@unipi.it and francesca.naretto@sns.it
  • Second part of the project consists in the assignment described here:
    1. Deadline: ….
  • Third part of the project consists in the assignment described here:
  1. Note that the document contains also rules for the delivery and final exam!
  2. Deadline: ….

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.

Oral Exam

  • Project presentation (with slides) – 10 minutes: mandatory for all the students
  • Open questions on the entire program: optional only for students opting for paper presentation.

Previous years

magistraleinformatica/dmi/start.txt · Ultima modifica: 23/09/2022 alle 13:17 (3 giorni fa) da Anna Monreale