Erinevus lehekülje "Data Mining and network analysis IDN0110" redaktsioonide vahel
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== Lecture 13 Privacy preserving data mining == | == Lecture 13 Privacy preserving data mining == | ||
[[Media:Lecture_13_DM2019_Privacy_preserving_data_mining.pdf |Slides]] | [[Media:Lecture_13_DM2019_Privacy_preserving_data_mining.pdf |Slides]] | ||
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| + | == Closed Book test 3 : December the 10th Usual lecture time == | ||
Redaktsioon: 5. detsember 2019, kell 13:55
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: Tuesdays 14:00-15:30 ICT-A1
Labs (practices): Tuesdays 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 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 ained.ttu.ee environment. Course enrollment (to ained.ttu.ee) process will be conducted during the first lecture/practice.
Lectures
Lecture 1 Introduction
Lecture 2 Similarity and Distance
Lecture 3 Cluster Analysis
Lecture 4 Classification
Closed Book test 1 : October the 1st Usual lecture time
Lecture 5 Anomaly and Outlier Analysis
Lecture 6 Association pattern mining
Lecture 7 Similarity and distance part II
Lecture 8 Mining Data Streams
Lecture 9 Mining Time series