Erinevus lehekülje "Data Mining and network analysis IDN0110" redaktsioonide vahel
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== Lecture 9 Time Series Mining == | == Lecture 9 Time Series Mining == | ||
[[Media:Lecture_09_DM2018_Mining_TimeSeries.pdf |Slides]] | [[Media:Lecture_09_DM2018_Mining_TimeSeries.pdf |Slides]] | ||
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| + | == Lecture 9 Time Series Mining == | ||
| + | [[Media:Lecture_10_DM2018_Mining_Data_Streams.pdf |Slides]] | ||
== Closed book test 1 will take place on 21.11.2018, usual lecture time == | == Closed book test 1 will take place on 21.11.2018, usual lecture time == | ||
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== Closed book test 2 will take place on 19.12.2018, usual lecture time == | == Closed book test 2 will take place on 19.12.2018, usual lecture time == | ||
Redaktsioon: 29. november 2018, kell 13:12
Fall 2018/2019
IDN0110 / ITI8730: Data Mining and network analysis Taught by: Sven Nõmm EAP: 6.0
Lectures: Wednesdays 14:00-15:30 ICT-A1 Labs: Thursdays 16:00-17:30 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
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 closed book tests. Each test gives 10% of the final grade.
- 3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade.
- final exam (gives 50 % 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 ained.ttu.ee environment. Course enrollment process will be conducted during the first lecture.