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Target Audience: bachelor/specialist degree students (3rd year and higher) majoring in Computer Science, Applied Mathematics, Bioinformatics and similar domains.
- Basic knowledge of Python. A good example of what you should be able to do: https://gitlab.erc.monash.edu.au/andrease/Python4Maths/tree/master
- Basic knowledge of Linear Algebra / Probability Theory / Statistics. Deep Learning book provides a great overview of the necessary topics:
- Linear Algebra: http://www.deeplearningbook.org/contents/linear_algebra.html
- Probability and Information Theory: http://www.deeplearningbook.org/contents/prob.html
- Numerical Computation: http://www.deeplearningbook.org/contents/numerical.html
Program Coordinator: Radoslav Neychev, Assistant Professor, Machine Learning course Lecturer, MIPT; Visiting Lecturer, Harbour.Space University, Barcelona, Spain; NLP course Lecturer, Big Data Academy (MADE) by Mail.ru group. ex. Yandex/CERN, ex. Raiffeisenbank
The educational program acquaints students with the modern state of Artificial Intelligence field and provides an opportunity to gain extensive practical experience and theoretical background in Machine Learning and Deep Learning, illustrated with examples from Natural Language Processing domain. Theoretical lectures are accompanied by practical seminars and individual assignments based on real problems from various industries.
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