Data Mining (ITI8730)

Allikas: Kursused
Redaktsioon seisuga 30. august 2024, kell 11:56 kasutajalt Sven (arutelu | kaastöö)
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Information for perspective students:

Lecture schedule and slides content are tentative. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!

The course is open to students with valid TalTech UniID! The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula.

Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd.

Those planning to use their own computers please install "R" and "R-studio".


Fall 2024

ITI8730: Data Mining and network analysis

Old code for this course is IDN0110

Taught by: Prof. Sven Nõmm

Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.

EAP: 6.0

Lectures: Tuesdays 12:00 - 13:30 ICT-702

Labs (practices): Thursdays 14:00 - 15:30 ICT-403

Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00)

Consultation: by appointment only Please do not hesitate to ask for appointment!!! For communication please use the following e-mail: sven.nomm@taltech.ee

Prerequisites to join the course

Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language.

Overview

The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:

  • Clustering
  • Classification
  • Association pattern mining
  • Outlier analysis

Main topics of the course:

  • Data types and Data Preparation
  • Similarity and Distances, Association Pattern Mining,
  • Cluster Analysis, Classification, Outlier analysis
  • Data streams, Text Data, Time Series, Discrete Sequences,
  • Graph Data, Social Network Analysis

Evaluation

  • 2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.
  • 3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.
  • final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.

Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).

Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA. Please note below are the slides from previous year. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment.

Lectures and Time line

05.09.23 Distance function

Slides

12.09.23 Cluster analysis I

Slides

19.09.23 Cluster analysis II

Slides

Slides (Practice)

26.09.23 Anomaly and outlier analysis

Slides

03.10.23 Classification I

Slides

10.10.23 Classification II

Slides

17.10.23 Regression analysis

Slides

24.10.23 Association Pattern mining

Slides

31.10.23 Closed Book Test I

07.11.23 Distance and Similarity II

Slides

14.11.23 Mining the Time series

Slides

21.11.23 Mining data streams

Slides

28.11.23 Text data mining

Slides

05.12.23 Graph data mining and Social analysis

Slides

Slides

12.12.23 Privacy preserving data mining

Slides

19.12.23 Closed Book Test II