Erinevus lehekülje "Data Mining (ITI8730)" redaktsioonide vahel
| 53. rida: | 53. rida: | ||
== Week 3 13.09.22 Cluster analysis II == | == Week 3 13.09.22 Cluster analysis II == | ||
| − | == Week 4 20.09.22 | + | == Week 4 20.09.22 Cluster analysis III == |
| − | == Week 5 27.09.22 | + | == Week 5 27.09.22 Outlier analysis == |
| − | == Week 6 | + | == Week 6 24.10.22 Classification I == |
| − | == Week 7 11.10.22 | + | == Week 7 11.10.22 Classification II == |
| − | == Week 8 18.10.22 | + | == Week 8 18.10.22 Regression == |
| − | == Week 9 | + | == Week 9 25.10.22 Association Pattern mining == |
== Week 9 TBC 27.10.22 Open book test I == | == Week 9 TBC 27.10.22 Open book test I == | ||
| − | == Week 10 TBC 01.11.22 Mining the Time series == | + | == Week 10 TBC 01.11.22 Distance and Similarity II == |
| + | |||
| + | == Week 10 TBC 03.11.22 Mining the Time series == | ||
== Week 11 TBC 08.11.22 Mining data streams == | == Week 11 TBC 08.11.22 Mining data streams == | ||
Redaktsioon: 23. august 2022, kell 14:34
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.