Introduction to Machine Learning

Abstract:

The course offers a gentle, hands-on introduction to the core principles and techniques of machine learning. The participants will be presented with the basic supervised machine learning models (linear models, trees and some instance-based techniques), along with the principles of their application (training, testing, cross-validation, regularization, model selection). Students will implement and apply most of the discussed algorithms using the interactive environment IPython. This will be followed by a discussion of the general principles underlying machine learning, such as statistical estimation methods and optimization techniques. Some extent of basic familiarity with programming and Python is expected from the participants. Previous knowledge of probability theory and linear algebra would be helpful and could ensure better understanding of the material.

About lecturer:

Mr. Konstantin Tretyakov,
Researcher, University of Tartu, Estonia.