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

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
11. rida: 11. rida:
 
EAP: 6.0
 
EAP: 6.0
  
Lectures:            Tuesdays        14:00-15:30  ICT-A1
+
Lectures:            Even weeks: Tuesdays        14:00-15:30  ICT-A1
 +
                      Odd weeks:  Thursdays      14:00-15:30  ICT-A1
  
Labs (practices):    Tuesdays      16:00-17:30  ICT-401
+
Labs (practices):    Wednesdays                  10:00-11:30  ICT-401
  
  
39. rida: 40. rida:
 
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: 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.
+
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 ==
 
[[Media:Lecture1_DM2019_Introduction.pdf ‎|Slides]]
 
 
 
== Lecture 2  Similarity and Distance  ==
 
[[Media:Lecture2_DM2019_Similarity_and_Distance.pdf ‎|Slides]]
 
 
 
== Lecture 3  Cluster Analysis  ==
 
[[Media:Lecture3_DM2019_Cluster_analysis.pdf ‎|Slides]]
 
 
 
== Lecture 4  Classification  ==
 
[[Media:Lecture4_DM2019_Classification.pdf ‎|Slides]]
 
 
 
 
== Closed Book test 1 : October the 1st Usual lecture time ==
 
 
 
== Lecture 5  Anomaly and Outlier Analysis  ==
 
[[Media:Lecture5_DM2019_Anomaly_and_Outlier_Analysis.pdf ‎|Slides]]
 
 
 
 
== Lecture 6  Association pattern mining  ==
 
[[Media:Lecture_06_DM2019_Association_Pattern_Mining.pdf ‎|Slides]]
 
 
 
== Lecture 7  Similarity and distance part II ==
 
[[Media:Lecture_07_DM2019_Similarity_and_Distance_2.pdf ‎|Slides]]
 
 
== Lecture 8  Mining Data Streams ==
 
[[Media:Lecture_08_DM2019_Mining_Data_Streams.pdf ‎|Slides]]
 
 
== Lecture 9  Mining Time series ==
 
[[Media:Lecture_09_DM2019_Mining_TimeSeries.pdf ‎|Slides]]
 
 
 
== Closed Book test 2 : November the 12th Usual lecture time ==
 
 
 
== Lecture 10  Text Data Mining ==
 
[[Media:Lecture_10_DM2019_TextDataMining.pdf ‎|Slides]]
 
 
== Lecture 11  Mining Graph Data ==
 
[[Media:Lecture_11_DM2019_Mining_Data_Graph_Data.pdf ‎|Slides]]
 
 
== Lecture 12  Social networks ==
 
[[Media:Lecture_12_DM2019_Social_Network_analysis.pdf ‎|Slides]]
 
 
== Lecture 13  Privacy preserving data mining ==
 
[[Media:Lecture_13_DM2019_Privacy_preserving_data_mining.pdf ‎|Slides]]
 
 
== Closed Book test 3 : December the 10th Usual lecture time ==
 

Redaktsioon: 28. august 2020, kell 12:06

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: Even weeks: Tuesdays 14:00-15:30 ICT-A1

                     Odd weeks:  Thursdays       14:00-15:30  ICT-A1

Labs (practices): Wednesdays 10:00-11:30 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 Moodle environment. Course enrollment process in Moodle TBA.

Lectures