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

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
 
(ei näidata sama kasutaja 46 vahepealset redaktsiooni)
1. rida: 1. rida:
Fall 2018/2019
+
Fall 2021/2022
 +
 
 +
ITI8730: Data Mining and network analysis
 +
 
 +
Old code for this course is IDN0110
  
IDN0110 / ITI8730: 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
  Lectures: Wednesdays      14:00-15:30 ICT-A1
+
                     
  Labs:    Thursdays       16:00-17:30  ICT-401
+
Labs (practices):    Thursdays 16:00 - 17:30  ICT-403
  
  
 
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!
 
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!
 
For communication please use the following e-mail: sven.nomm@ttu.ee
 
For communication please use the following e-mail: sven.nomm@ttu.ee
 +
  
 
==Overview ==
 
==Overview ==
28. 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 3 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 ained.ttu.ee environment. Course enrollment process will be conducted during the first lecture.
+
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.
  
 
=Lectures =
 
=Lectures =
== Lecture 1  Introduction and data preparation ==
 
[[Media:Lecture1_DM2018_Introduction.pdf ‎|Slides]]
 
 
== Lecture 2  Similarity and distance ==
 
[[Media:Lecture2_DM2018_Similarity_and_Distance.pdf ‎|Slides]]
 
 
== Lecture 3  Cluster analysis ==
 
[[Media:Lecture3_DM2018_Cluster_analysis.pdf ‎|Slides]]
 
 
== Lecture 4  Classification ==
 
[[Media:Lecture4_DM2018_Classification.pdf ‎|Slides]]
 

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