Semester: 4
General Foundation
ECTS: 6
Hours per week: 3
Professor: T.B.D.
Teaching style: Face to face, tutorials and project work
Grading: Final written exam (70%), Group exercises (30%)
Activity | Workload |
---|---|
Lectures | 26 |
Tutorials | 13 |
Group work on laboratory projects | 48 |
Individual study | 63 |
Course total | 150 |
The aim of the course is to present Data Mining techniques, as well as their applications. It will provide and introduction to the field of analytics, and therefore the extensive use of data, statistical and quantitative analysis, exploratory and predictive models to mine and discover unexpected but ueful glimpses of previously unkown information. We discuss standard data mining algorithms that can be applied both on structured and unstructured data and experience their impact on decision making situations.
Upon successful completion of the course students:
J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011.
P. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Pearson, 2018, 2nd edition.
M. Zaki, W. Meira Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, 2017.
I. Witten, E. Frank, Μ. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kaufmann, 2011.
Related scientific journals:
Data Mining and Knowledge Discovery, Springer.
IEEE Transactions on Knowledge and Data Engineering, IEEE.
ACM Transactions on Knowledge Discovery from Data, ACM.
SIGKDD Explorations, ACM.
Intelligent Data Analysis, IOS Press.
IEEE Transactions on Neural Networks and Learning Systems, IEEE
The aim of the course is to present Data Mining techniques, as well as their applications. It will provide and introduction to the field of analytics, and therefore the extensive use of data, statistical and quantitative analysis, exploratory and predictive models to mine and discover unexpected but ueful glimpses of previously unkown information. We discuss standard data mining algorithms that can be applied both on structured and unstructured data and experience their impact on decision making situations.
Upon successful completion of the course students:
J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011.
P. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Pearson, 2018, 2nd edition.
M. Zaki, W. Meira Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, 2017.
I. Witten, E. Frank, Μ. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kaufmann, 2011.
Related scientific journals:
Data Mining and Knowledge Discovery, Springer.
IEEE Transactions on Knowledge and Data Engineering, IEEE.
ACM Transactions on Knowledge Discovery from Data, ACM.
SIGKDD Explorations, ACM.
Intelligent Data Analysis, IOS Press.
IEEE Transactions on Neural Networks and Learning Systems, IEEE