By completing this course, the student acquired basic knowledge about methods of machine learning. The student also acquired practical skills in data analysis and graphical interpretation using the Python programming language. By completing the course, the student is competent in the design of advanced regression and classification models of different datasets.
Prerequisites for registration:
Recommended or required reading:
Guido, S.: Introduction to Machine Learning with Python, O'Reilly Media, Inc, USA, 2016, ISBN: 9781449369415
Chollet, F.: Deep Learning with Python, Manning Publications, 2017, ISBN: 1617294438
McKinney, W.: Python for Data Analysis, 2nd edition, O'Reilly Media, Inc, USA, 2017, ISBN: 9781491957660
Planned learning activities and teaching methods:
Contact teaching: exercises 26h
Contactless teaching: preparation for exercises (12h), preparation of final project (12h)
Assesment methods and criteria:
Evaluation is based 50% on the work during the semester, 50% from the final project evaluation. The rating is based on the standard FCHPT scale.
Language of instruction:
Institute of Information Engineering, Automation and Mathematics was established in 1.1.2006 from two departments: Department of Information Engineering and Process Control and Department of Mathematics.