Observing the world and compressing these observations into compact rules have been of great importance to humankind for ages. Nowadays we collect and generate a lot of data, so big that no human can analyze it. Machine learning is a field of science that is responsible for designing computer algorithms capable of learning important patterns directly from the large volumes of data without being explicitly programmed to. In this course, we are going to look into principles and techniques that are at the core of machine learning. Topics will include notions of supervised and unsupervised learning; classification, regression, clustering and dimensionality reduction methods; deceptive effects of overfitting and ways to estimate models’ generalization power. Separately, we will look into timesereis modelling.
At the end of this course, the student should be able to:
- Review different classes of Machine Learning methods;
- Learn details of inner workings of some of the most important Machine Learning methods;
- Learn pros and cons and potential application domain of each method;
- Learn most common problems that are encountered when training Machine Learning models and ways to prevent them;
- Learn the fundamental differences between timeseries and other types of data;
- Learn modelling methods specific to timeseries.
Lecturer: Dmytro Fishman, Ph.D., a researcher in the field of machine learning.
Exams & certification:
After the successful completion of the course, the participants will get a certificate.
Price: UAH 24 000
Please fill in the form below and our Program Coordinator will contact you as soon as possible.