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

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
 
(ei näidata sama kasutaja 60 vahepealset redaktsiooni)
1. rida: 1. rida:
Fall 2017/2018
+
Fall 2021/2022
 +
 
 +
ITI8730: Data Mining and network analysis
 +
 
 +
Old code for this course is IDN0110
  
IDN0110: Data Mining and network analysis
 
 
Taught by: Sven Nõmm
 
Taught by: Sven Nõmm
 +
 
EAP: 6.0
 
EAP: 6.0
 +
 +
Lectures:  Tuesdays 14:00  - 15:30 ICT-315
 +
                     
 +
Labs (practices):    Thursdays  16:00 - 17:30  ICT-403
  
Time and place: NB! Note time and places of the lectures on even weeks have been changed!!!
 
  
  Lectures: Wednesdays      16:00-17:30  ICT-A1
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Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!
  Labs:    Thursdays      16:00-17:30  ICT-401
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For communication please use the following e-mail: sven.nomm@ttu.ee
  
 
Consultation: '''by appointment only'''  Thursdays 17.30-18-30
 
Additional information: sven.nomm@ttu.ee
 
  
 
==Overview ==
 
==Overview ==
29. rida: 33. rida:
  
 
==Evaluation==
 
==Evaluation==
*2x mandatory closed book tests. Each test gives 10% of the final grade.
+
*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.
+
*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.
+
*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 4 home assignments are accepted (graded as 51 or higher).
+
Exam prerequisites: All 3 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.
  
Moodle environment will be set up after the second lecture.
+
=Lectures =

Viimane redaktsioon: 23. august 2021, kell 09:16

Fall 2021/2022

ITI8730: Data Mining and network analysis

Old code for this course is IDN0110

Taught by: Sven Nõmm

EAP: 6.0

Lectures: Tuesdays 14:00 - 15:30 ICT-315

Labs (practices): Thursdays 16:00 - 17:30 ICT-403


Consultation: by appointment only Please do not hesitate to ask for appointment!!! For communication please use the following e-mail: sven.nomm@ttu.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: All 3 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.

Lectures