TIES4450 Data Mining and Machine Learning (5 cr)

Study level:
Advanced studies
Grading scale:
0-5
Language:
English, Finnish
Responsible organisation:
Faculty of Information Technology
Curriculum periods:
2020-2021, 2021-2022, 2022-2023, 2023-2024

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Introduces the main machine learning / data mining methods and algorithms; knowledge of the basic concepts related to the overall knowledge discovery process.

Description

The course introduces the methods of data mining for large and incomplete data masses. The course starts with the basic concepts, definitions and challenges related to the development and application of data mining methods. Learn about the various stages of the process of knowledge search (KDD) and the methods applied in them. The methods used for different types of data mining problems are reviewed. Get acquainted with the application of methods in practical materials. Exercises and project work are done using Matlab software.

Learning outcomes

Understanding of the main machine learning / data mining methods and algorithms; knowledge of the basic concepts related to the overall knowledge discovery process

Description of prerequisites

Basic programming skills and knowledge of mathematical and algorithmic concepts are required.

Study materials

M. J. Zaki and W. Meira Jr.: “Data Mining and Analysis – Fundamental Concepts and Algorithms”, Cambridge University Press, 2014

Hand, Manilla and Smyth: “Principles of Data Mining”, MIT Press
Hastie, Tibshirani and Friedman: “The Elements of Statistical Learning”, Springer, 2017

Principles of Data Mining, D. Hand, H. Mannila, and P. Smyth, MIT Press, 2001. P-N.

Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison Wesley, 2005. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2006. Wang, X.

Completion methods

Method 1

Select all marked parts
Parts of the completion methods
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Participation in teaching (5 cr)

Type:
Participation in teaching
Grading scale:
0-5
Evaluation criteria:
Exam
Language:
English, Finnish
Study methods:

Exam, exercises and project work 

Teaching