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

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
 
(ei näidata sama kasutaja 19 vahepealset redaktsiooni)
1. rida: 1. rida:
Fall 2019/2020
+
Fall 2021/2022
  
 
ITI8730: Data Mining and network analysis
 
ITI8730: Data Mining and network analysis
6. rida: 6. rida:
  
 
Taught by: Sven Nõmm
 
Taught by: Sven Nõmm
 
Practice given by Alejandro Guerra Manzanares
 
  
 
EAP: 6.0
 
EAP: 6.0
 +
 +
Lectures:  Tuesdays 14:00  - 15:30 ICT-315
 +
                     
 +
Labs (practices):    Thursdays  16:00 - 17:30  ICT-403
  
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
  
 
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 ==
 
==Overview ==
37. rida: 36. rida:
 
*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.
 
*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.
 
*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 (to ained.ttu.ee) process will be conducted during the first lecture/practice.
 
 
 
  
=Lectures =
+
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.
== Lecture 1  Introduction ==
 
[[Media:Lecture1_DM2019_Introduction.pdf ‎|Slides]]
 
 
 
=Lectures =
 
== Lecture 2  Similarity and Distance  ==
 
[[Media:Lecture2_DM2019_Similarity_and_Distance.pdf ‎|Slides]]
 
 
 
=Lectures =
 
== Lecture 3  Cluster Analysis  ==
 
[[Media:Lecture3_DM2019_Cluster_analysis.pdf ‎|Slides]]
 
 
 
=Lectures =
 
== Lecture 4  Classification  ==
 
[[Media:Lecture4_DM2019_Classification.pdf ‎|Slides]]
 
 
 
=Closed book test 1
 
== October the 1st Usual lecture time ==
 
 
 
=Lectures =
 
== Lecture 5  Anomaly and Outlier Analysis  ==
 
[[Media:Lecture5_DM2019_Anomaly_and_Outlier_Analysis.pdf ‎|Slides]]
 
 
 
 
 
=Lectures =
 
== Lecture 6  Association pattern mining ==
 
[[Media:Lecture_06_DM2019_Association_Pattern_Mining.pdf ‎|Slides]]
 
  
 
=Lectures =
 
=Lectures =
== Lecture 7  Similarity and distance part II ==
 
[[Media:Lecture_07_DM2019_Similarity_and_Distance_2.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