Erinevus lehekülje "Data Mining and network analysis IDN0110" redaktsioonide vahel

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== Lecture 13  Privacy preserving data mining ==
 
== Lecture 13  Privacy preserving data mining ==
 
[[Media:Lecture_13_DM2019_Privacy_preserving_data_mining.pdf ‎|Slides]]
 
[[Media:Lecture_13_DM2019_Privacy_preserving_data_mining.pdf ‎|Slides]]
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== Closed Book test 3 : December the 10th Usual lecture time ==

Redaktsioon: 5. detsember 2019, kell 13:55

Fall 2019/2020

ITI8730: Data Mining and network analysis

Old code for this course is IDN0110

Taught by: Sven Nõmm

Practice given by Alejandro Guerra Manzanares

EAP: 6.0

Lectures: Tuesdays 14:00-15:30 ICT-A1

Labs (practices): Tuesdays 16:00-17:30 ICT-401


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

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,
  • Spatial Data, Graph Data, Web Data, Social Network Analysis

Evaluation

  • 3x 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 40 % of the final grade): Written report on assigned topic + discussion with lecturer.

Exam prerequisites: both 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 ained.ttu.ee environment. Course enrollment (to ained.ttu.ee) process will be conducted during the first lecture/practice.


Lectures

Lecture 1 Introduction

Slides


Lecture 2 Similarity and Distance

Slides


Lecture 3 Cluster Analysis

Slides


Lecture 4 Classification

Slides


Closed Book test 1 : October the 1st Usual lecture time

Lecture 5 Anomaly and Outlier Analysis

Slides


Lecture 6 Association pattern mining

Slides


Lecture 7 Similarity and distance part II

Slides

Lecture 8 Mining Data Streams

Slides

Lecture 9 Mining Time series

Slides


Closed Book test 2 : November the 12th Usual lecture time

Lecture 10 Text Data Mining

Slides

Lecture 11 Mining Graph Data

Slides

Lecture 12 Social networks

Slides

Lecture 13 Privacy preserving data mining

Slides

Closed Book test 3 : December the 10th Usual lecture time