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HSE Launches Two New Specializations in Computer Science on Coursera

‘Introduction to Discrete Mathematics for Computer Science’ and ‘Advanced Machine Learning’ are English-language specializations, both created with the support of the top Russian IT firm Yandex, while the specialization ‘Introduction to Discrete Mathematics for Computer Science’ was developed with the involvement of the University of California at San Diego.

The authors of ‘Introduction to Discrete Mathematics for Computer Science’ are Mikhail Levin (HSE, Yandex), Vladimir Podolskii (HSE), Alexander Kulikov (University of California, San Diego), and Alexander Shen, Guest Lecturer from the French National Center for Scientific Research (CNRS).

In addition to the study of mathematical aspects, the authors aim to help students understand the nature of proof from a mathematical point of view.

‘It is important that students learn about different methods and ideas of reasoning, as well as the ability to distinguish proofs and infer their own ones,’ says Vladimir Podolskii, Head of the Big Data and Information Retrieval School. He notes: ‘It’s not a trivial problem to teach these skills online, as there are fewer opportunities for feedback than during full-time courses. Bearing this in mind, we have included interactive tasks and puzzles in our courses. It’s interesting experience to solve them, as they make you think about the possibilities of a particular structure, see the obstacles to developing arguments or models, and notice key ideas, which later can be transformed into proofs.’

The ‘Advanced Machine Learning’ specialization was developed by a team of 22 expert authors. It includes seven courses.

The first course, ‘Introduction to Deep Learning’, introduces students to deep neural networks and their architectures for the overall purpose of analyzing images and texts. Other courses within this specialization are connected with this concept in one way or another. 

Furthermore, three courses ‘Introduction to Reinforcement Learning’, ‘Deep Learning for Computer Vision’ and ‘Natural Language Processing’ are dedicated to specific issues related to machine learning.

The course ‘Bayesian Methods for Machine Learning’, which was created with the support of the International Laboratory of Deep Learning and Bayesian Methods, provides instruction on the basics of the Bayesian approach and its possible applications for training and developing deep neural networks.

Furthermore, two courses are focused on practical issues. For instance, while studying the course ‘How to Win a Data Science Competition: Learn from Top Kagglers’, the winners of competitions in data analysis speak about using advanced machine learning methods and the best ways of achieve optimal results in practical tasks. For instance, when taking part in the ‘Addressing Large Hadron Collider Challenges by Machine Learning’ course, students learn approaches to solving complex tasks, which often might not work with standard machine learning methods, or whereby the subject may require very specific models. In particular, students get to investigate and analyze data from the Large Hadron Collider.

‘Data science is rapidly developing, and education doesn’t always catch up,’ says Evgeny Sokolov, Deputy Head of the Big Data and Information Retrieval School. He states: ‘At the start of 21st century, classic machine learning methods were successfully used in business, but courses in machine learning have only become widespread in recent years. Certain new areas, for example, deep learning or reinforcement learning, are only now emerging and appearing in university programmes. And with our specialization, students can learn about the latest advances in artificial intelligence, as well as be capable of applying the latest machine learning methods and gain experience in solving problems with respect to non-standard situations.’

Full list of HSE online courses



See also:

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