Objectives and competences
The goal of this course is for the student to acquire the basic knowledge of green machine learning and data processing with the help of interactive tools and cloud services. The focus will be on efficient and economical use of computer resources in all phases of machine learning: from data collection to the final use of machine learning results.
Content (Syllabus outline)
• Introduction to intelligent systems (basic concepts, machine learning approaches, green aspects of machine learning)
• Basics of data collections (green approach to databases, data types, attribute types, data organization, database formats, data visualization)
• Data preparation (data processing tools, green data, data pre-processing, data filtering, statistical data analysis)
• Machine learning tools (interactive machine learning tools, machine learning libraries, integration of machine learning tools)
• Cloud machine learning (green aspects of cloud machine learning, cloud machine learning services, cloud services and integration)
• Visualization and evaluation of results (interpretation of results, metrics for interpretation of results, visualization methods and approaches)
Learning and teaching methods
• lectures,
• lab work.
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
• understand the use of green machine learning,
• data processing into an appropriate format with appropriate tools,
• using tools and cloud services for green machine learning needs.
Transferable/Key skills and other attributes:
• Data processing: data processing for the needs of green application and statistical review of data,
• Selection and use of machine learning algorithms: use of suitable machine learning techniques with the help of interactive tools,
• Interpretation and visualization of results: the ability to interpret and visualize results and the ability to judge the appropriateness of results.
Readings
• J. Han, M. Kamber, J.Pei: Data Mining: Concepts and Techniques, Third Edition, Elsevier, Morgan Kaufmann Publishers, 2012.
• H. Witten, E. Frank, M. A. Hall: Data Mining, Practical Machine Learning Tools and Techniques, Third Edition, Morgan Kaufmann Publishers, 2011.
• M. Zorman, V. Podgorelec, M. Lenič, P. Povalej, P. Kokol, A. Tapajner: Inteligentni sistemi in profesionalni vsakdan, Univerza v Mariboru, Center za Interdisciplinarne in multidisciplinarne raziskave in študije UM, Maribor, 2003.
• E. Bisong Building machine learning and deep learning models on Google cloud platform. Berkeley, CA: Apress, 2019.
Prerequisits
No prerequisites.
