Fundamentals of machine learning (AM)
Type: For the student's choice
Department: computational mathematics
Curriculum
| Semester | Credits | Reporting |
| 6 | 4 | Setoff |
Lectures
| Semester | Amount of hours | Lecturer | Group(s) |
| 6 | 32 | Associate Professor Yu. A. Muzychuk | PMp-31, PMp-32 |
Laboratory works
| Semester | Amount of hours | Group | Teacher(s) |
| 6 | 32 | PMp-31 | Associate Professor Yu. A. Muzychuk, Borachok I. V. |
| PMp-32 | Associate Professor Yu. A. Muzychuk, Borachok I. V. |
Course description
The course “Fundamentals of Machine Learning” covers the following sections: regression problems, classification problems and problems of unsupervised machine learning. When considering these topics, the main focus is on the mathematical formulation of problems, the choice of solution methods and the detailed description and programming of appropriate algorithms. The presentation of the material is carried out using modern terms and concepts in the field of information technology. The aim of the course is to get acquainted with the available methods of solving various problems in the field of machine learning and to explain important aspects of their use.
Recommended Literature
- Goodfellow I. Deep Learning / Ian Goodfellow, Yoshua Bengio, Aaron Courville. – MIT Press, 2016. – WWW: http://www.deeplearningbook.org.
- Witten I. Data Mining: Practical Machine Learning Tools and Techniques / Ian Witten, Eibe Frank, Mark A. Hall. – Morgan Kaufmann, 2016. – 654p.
- Mitchell T. Machine Learning / Tom M. Mitchell. – McGraw-Hill Education, 1997. – 432p.
- Flach P. Machine Learning: The Art and Science of Algorithms that Make Sense of Data / Peter Flach. – Cambridge University Press, 2012. – 409p.
- Raschka S. Python Machine Learning / Sebastian Raschka. – Packt Publishing, 2015. – 454p.
- Géron A. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Techniques and Tools to Build Learning Machines / Aurélien Géron. – O’Reilly Media, 2018. – 566p.