Erinevus lehekülje "Data Mining and network analysis IDN0110" redaktsioonide vahel
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| + | == Lecture 1 Distance function == | ||
| + | [[Media:Lecture1_DM2020_Introduction_distance_function.pdf |Slides]] | ||
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| + | == Lecture 2 Distance function == | ||
| + | [[Media:Lecture2_DM2020_Cluster_analysis.pdf |Slides]] | ||
Redaktsioon: 8. september 2020, kell 08:30
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 / 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
You can join the course in Moodle using "UseR!" as key.
For remote participation please login into MS Teams using your TalTech UniID and joint the team ITI8730-Data Mining 2020 using link below: https://teams.microsoft.com/l/channel/19%3a13eb84f2dd0b42ef8e589501caf01e02%40thread.tacv2/General?groupId=5e74549c-9e08-4b6b-b16f-7c2df1b624b6&tenantId=3efd4d88-9b88-4fc9-b6c0-c7ca50f1db57
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.