We invite you to the new course “Supervised Machine Learning”, which will expand the knowledge of basic machine learning methods with manual feature construction, namely methods such as: naive Bayes classifier, support vector machines, random forest, etc.
- Level: intermediate
- English language
- Format: online
- Course duration: 30 hours (1 ECTS)
- Start: from 04/19/2024 (independent passage)
- Availability: self-study based on video and text materials on the KAU online platform, passing the final test;
- Target audience: Students and graduate students of computer science, applied physics, and materials science.
Teachers:
- Ph.D., Vitaly Timchishin, junior researcher at the Kyiv Academic University (KAU), junior researcher at the Institute of Theoretical Physics named after. MM. Bogolyubova;
- Ph.D. Volodymyr Bezguba, senior researcher at KAU, head of the KAU Data Research and Machine Learning Laboratory.
Required knowledge
Basic knowledge of higher mathematics and programming obtained upon receipt of a bachelor’s degree. Basic knowledge of Python.
Learning outcomes
- Knowledge of basic machine learning methods with manual feature construction, namely methods such as: naive Bayes classifier, support vector machines, random forest, etc.
- Ability to select and train classification, regression and clustering algorithms on labeled data.
The course is free. After passing it, all participants who complete the program and pass the tests will receive certificates with the amount of ECTS credits (1 credit).
The preparation and teaching of courses was carried out by DNU “Kiev Academic University” within the framework of the BOOSTalent project, funded by the European Institute of Technology and Innovation.