Machine learning techniques are at the forefront of modern data science and artificial intelligence. The curriculum of the program contains a balanced combination of topics developed very recently together with in-depth teaching of mathematical foundations, such as advanced linear algebra, optimization, high-dimensional statistics, etc.
This track is also available in network form with the Moscow Institute of Physics and Technology.A successful graduate of this track will be able to:
- understand and formulate complex real-world tasks as data analysis problems
- contribute to the development of the next-generation machine learning software competitive with or superior to the existing examples of software in critical and emerging application fields
- apply relevant software tools, algorithms, data models, and computational environments for the solution of real-world problems
Compulsory courses: - Numerical Linear Algebra
- Machine Learning
- Deep Learning
Recommended electives:- Introduction to Data Science
- Introduction to Artificial Intelligence
- Computational Imaging
- Foundations of Software Engineering
- Efficient Algorithms and Data Structures
- Theoretical Methods of Deep Learning
- Convex Optimization and Applications
- Introduction to Computer Vision
- Statistical Natural Language Processing
- Principles of Applied Statistics
- Perception in Robotics
- Introduction to Digital Agro
- Advanced Statistical Methods
- Statistical Learning Theory
- Geometric Computer Vision
- Biomedical Imaging and Analytics
- Geometrical Methods of Machine Learning
- Neural Natural Language Processing
- Planning Algorithms in Artificial Intelligence
- Bayesian Methods of Machine Learning
- Matrix and Tensor Factorizations
- Neuroimaging and Machine Learning for Biomedicine
- Reinforcement Learning
- Models of Sequential Data
- More recommended courses in the Skoltech Course Catalog>>
Entrepreneurship and Innovation: - Leadership for Innovators
- Hack Lab: Laboratory for Ideas
- Startup Workshop
- Ideas to Impact: Foundations for Commercializing Technological Advances
- Intellectual Property, Technological Innovation and Entrepreneurship
- Technological Innovations: from Research Results to a Commercial Product
- Developing Products and Services through Design Thinking
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