Erinevus lehekülje "Data Mining (ITI8730)" redaktsioonide vahel

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
1. rida: 1. rida:
Fall 2021/2022
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Fall 2022/2023
  
 
ITI8730: Data Mining and network analysis
 
ITI8730: Data Mining and network analysis
5. rida: 5. rida:
 
Old code for this course is IDN0110
 
Old code for this course is IDN0110
  
'''Dear students, There were a problem with registration for the exam for a particular date.  The problem is now fixed and besides 17.01  also 18.01 and 7.01 are available. Please accept my apology for the inconvenience.  '''
 
 
Taught by: Sven Nõmm
 
Taught by: Sven Nõmm
  
 
EAP: 6.0
 
EAP: 6.0
 
   
 
   
Lectures:  Tuesdays 14:00 - 15:30 SOC-414
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Lectures:  Tuesdays 16:30 - 18:00 ICT-315
 
                        
 
                        
Labs (practices):    Thursdays  16:00 - 17:30  ICT-403
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Labs (practices):    Thursdays  14:00 - 15:30  ICT-401
  
Link to join MS Teams https://teams.microsoft.com/l/channel/19%3a2PRNmKxRN9GR2oG68vo3_-25RYYTxAbZrA5dJ0YfoAA1%40thread.tacv2/General?groupId=9dde2da7-1d60-49a4-ac28-2d065338e369&tenantId=3efd4d88-9b88-4fc9-b6c0-c7ca50f1db57
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Link to join MS Teams  
 
It is advisable to use MS Teams client application and log in with TalTech account.
 
It is advisable to use MS Teams client application and log in with TalTech account.
  
To join the course in talTech Moodle please use code "UseR!!!"
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Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!
 
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!
40. rida: 39. rida:
  
 
==Evaluation==
 
==Evaluation==
*3x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.
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*2x mandatory open 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.
 
*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.
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*final exam (gives 50 % 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).
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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.
 
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 =
== Week 1  Distance function ==
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== Week 1  30.08.22 Distance function ==
[[Media:Lecture1_DM2021_Introduction_distance_function.pdf ‎|Slides]]
 
 
 
== Week 2  Cluster analysis ==
 
[[Media:Lecture_02_DM2021_Cluster_analysis.pdf ‎|Slides]]
 
 
 
== Week 3  Cluster analysis ==
 
[[Media:Lecture_03_DM_2021_Cluster_analysis_EM.pdf ‎|Slides]]
 
 
 
== Week 4  Outlier analysis ==
 
[[Media:Lecture_04_DM2021_Anomaly_and_Outlier_Analysis.pdf ‎|Slides]]
 
 
 
== Week 5  Classification ==
 
[[Media:Lecture_5_DM2021_Classification.pdf ‎|Slides]]
 
  
== Week 6 Closed Book I Test 05.10.2021  ==
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== Week 2 06.09.22 Cluster analysis I ==
Home assignment defense 07.10.2021
 
  
== Week 7 The lecture on 12.10.2021 will be given in ONLINE mode only  ==
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== Week 3 13.09.22 Cluster analysis II ==
General analysis of the results of closed book test 1, continuation of the lecture 5, interesting discussion of a few recent trends.
 
  
== Week 8 Classification ==
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== Week 4 20.09.22 Outlier analysis ==
[[Media:Lecture_06_Classification_2_DM_2021.pdf ‎|Slides]]
 
  
== Week 9 Regression ==
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== Week 5 27.09.22 Classification I ==
[[Media:Lecture_07_Data_preparation_regression_DM_2021.pdf ‎|Slides]]
 
  
== Week 10 Associative pattern mining ==
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== Week 6  04.10.22 Classification II ==
[[Media:Lecture_08_DM2021_Association_Pattern_Mining.pdf ‎|Slides]]
 
  
Make-up test 1 Online only; November the 4th  16:00
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== Week 7 11.10.22 Regression ==
  
== Week 11 Tests ==
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== Week 8 18.10.22 Association Pattern mining ==
09.11.21 14:00 defense of the home assignments (hybrid). Online options are also available in the evening!
 
11.11.21  Opened book test online!
 
  
== Week 12 Similarity and distance II ==
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== Week 9 25.10.22 Distance and Similarity II ==
[[Media:Lecture_09_DM2021_Similarity_and_Distance_2.pdf ‎|Slides]]
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== Week 9  27.10.22 Open book test I ==
  
== Week 13 Mining Time Series and Mining Data Streams ==
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== Week 10  01.11.22 Mining the Time series ==
'''NB!!! 23.11.2021 Lecture will start 14:15'''
 
  
[[Media:Lecture_10_DM2021_Mining_TimeSeries.pdf ‎|Slides]]
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== Week 11  08.11.22 Mining data streams ==
  
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== Week 12  15.11.22 Text data mining ==
  
[[Media:Lecture_11_DM2021_Mining_Data_Streams.pdf ‎|Slides]]
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== Week 13  22.11.22 Graph data mining ==
  
== Week 14  Mining text data ==
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== Week 14  29.11.22 Social networks analysis==
[[Media:Lecture_12_DM2021_TextDataMining.pdf ‎|Slides]]
 
  
== Week 15  Graph data mining and social networks analysis ==
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== Week 15  06.12.22 Privacy preserving data mining ==
[[Media:Lecture_13_DM2021_Mining_Data_Graph_Data.pdf ‎|Slides]]
 
[[Media:Lecture_14_DM2021_Social_Network_analysis.pdf ‎|Slides]]
 
[[Media:Lecture_15_DM2021_Privacy_preserving_data_mining.pdf ‎|Slides]]
 
  
== Week 16 Home Assignment defense on 14.12.2021 and Open Book Test 3 on 16.12.2021 ==
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== Week 16 13.12.22 Open book test 2 ==
No lecture or practice streaming this week!
 

Redaktsioon: 23. august 2022, kell 10:07

Fall 2022/2023

ITI8730: Data Mining and network analysis

Old code for this course is IDN0110

Taught by: Sven Nõmm

EAP: 6.0

Lectures: Tuesdays 16:30 - 18:00 ICT-315

Labs (practices): Thursdays 14:00 - 15:30 ICT-401

Link to join MS Teams It is advisable to use MS Teams client application and log in with TalTech account.


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

Evaluation

  • 2x mandatory open 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.

Lectures

Week 1 30.08.22 Distance function

Week 2 06.09.22 Cluster analysis I

Week 3 13.09.22 Cluster analysis II

Week 4 20.09.22 Outlier analysis

Week 5 27.09.22 Classification I

Week 6 04.10.22 Classification II

Week 7 11.10.22 Regression

Week 8 18.10.22 Association Pattern mining

Week 9 25.10.22 Distance and Similarity II

Week 9 27.10.22 Open book test I

Week 10 01.11.22 Mining the Time series

Week 11 08.11.22 Mining data streams

Week 12 15.11.22 Text data mining

Week 13 22.11.22 Graph data mining

Week 14 29.11.22 Social networks analysis

Week 15 06.12.22 Privacy preserving data mining

Week 16 13.12.22 Open book test 2