Introduction to Machine Learning 2017

Introduction to Machine Learning 2017

Course Description

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. To make the learning process interactive and gained skills more practical we will implement many of the mentioned algorithms in Python using Jupyter Notebooks as an interactive environment.


Prerequisites include prior experience with Python programming, understanding of basic calculus, linear algebra probability theory.

Course tools

Python, Jupyter notebook, brain power.

You will have to install Python and Jupyter notebooks prior to the course. There are several ways to do it, but recommended way for most users is to install Anaconda Python. Please, use the following link to download an installer (with Python 2.7). You should be able to find Windows, macOS and Linux compatible versions of installers available. NB! please, download Anaconda with the support of Python 2.7, to avoid clashes with a code that we are going to use in this course.


Mr. Dmytro Fishman
Junior Researcher, PhD candidate at the University of Tartu

Dmytro is a Junior Researcher and a PhD candidate at the University of Tartu. His current research is focused on applying machine learning and data mining methods to biological data. For his PhD thesis, Dmytro is building an automatic tool for analysing protein microarray experiments in immunological studies. He and his colleagues use Deep Learning on various biological data, including genomic data and microscopy images. Also Dmytro has experience teaching various machine learning related subjects both at the University of Tartu and as an invited lecturer in companies. He is a certified trainer in Data Carpentry and Software Carpentry organisations that organise and carry out trainings for scientists in the core data science skills around the world.

Fields of interests: Data Mining, Machine Learning, Bioinformatics, Image Recognition, Deep Learning, Advanced Algorithms.

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