Duration: 8-10 h

This course is intended for students, who are curious about data science and machine learning, but do not have deep background in mathematics. Main focus of this introductory workshop is various statistical-learning techniques with the predictive purpose - in other words, the fascinating field of “supervised machine learning”. This course will help participants to gain an intuition about general methods in the field and apply them on practice. We will discuss how to choose proper methods and prepare data to avoid fallacies, how to build, improve and compare models, and at last but not the least, how to understand them and “tell the story” of your results. We will be using an open source software R that is one of the most popular analysis tools with the broad “fan-zone”.

Materials of the course

Tutor

Ms. Anna Leontjeva

Country: Estonia

Place of employment: Software Technology and Applications Competence Center, Tartu, Estonia

Affiliation: researcher

Spheres of interests: machine learning, data mining, applied statistics, statistical learning

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