Erinevus lehekülje "Data Mining (ITI8730)" redaktsioonide vahel
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| 1. rida: | 1. rida: | ||
| − | Fall | + | 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 | ||
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Taught by: Sven Nõmm | Taught by: Sven Nõmm | ||
EAP: 6.0 | EAP: 6.0 | ||
| − | Lectures: Tuesdays | + | Lectures: Tuesdays 16:30 - 18:00 ICT-315 |
| − | Labs (practices): Thursdays | + | Labs (practices): Thursdays 14:00 - 15:30 ICT-401 |
| − | Link to join MS Teams | + | 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. | ||
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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== | ||
| − | * | + | *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 | + | *final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer. |
| − | Exam prerequisites: All | + | 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 == | + | == Week 1 30.08.22 Distance function == |
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| − | == Week | + | == Week 2 06.09.22 Cluster analysis I == |
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| − | == Week | + | == Week 3 13.09.22 Cluster analysis II == |
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| − | == Week | + | == Week 4 20.09.22 Outlier analysis == |
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| − | == Week | + | == Week 5 27.09.22 Classification I == |
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| − | == Week 10 | + | == Week 6 04.10.22 Classification II == |
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| − | + | == Week 7 11.10.22 Regression == | |
| − | == Week | + | == Week 8 18.10.22 Association Pattern mining == |
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| − | == Week | + | == Week 9 25.10.22 Distance and Similarity II == |
| − | + | == Week 9 27.10.22 Open book test I == | |
| − | == Week | + | == Week 10 01.11.22 Mining the Time series == |
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| − | + | == 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 | + | == Week 14 29.11.22 Social networks analysis== |
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| − | == Week 15 | + | == Week 15 06.12.22 Privacy preserving data mining == |
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| − | == Week 16 | + | == Week 16 13.12.22 Open book test 2 == |
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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.