Participants of the stream Applied Computer Science will learn about the most effective machine learning and artificial intelligence techniques. What is more important, you'll learn not only theoretical bases of ML and AI, but also will acquire practical know-how needed to quickly and powerfully apply those techniques to solve new problems.

Following areas would be covered:

• supervised learning (classification analysis, neural networks, support vector machines);
• unsupervised learning (clustering, dimensionality reduction, kernel methods);
• learning theory (bias/variance tradeoffs; VC theory; large margins);
• search heuristics (uninformed and informed search strategies; meta-­heuristics);
• probabilistic graphical modeling (bayesian networks, hidden Markov chains and models);
• robotics (construction, arduino programming, control theory, computer vision, decision making).

All the topics will be based on applications of ML and AI, such as robotics control, data mining, search games, bioinformatics, text and web data processing.

We also will construct a robot that will be able to discover terrain, build its map, and go from one specified point to another. Final test of the robot will be performed in the KPI park.

Courses of the stream are:

• Search Heuristics(13 h)
• Statistical Classification in Machine Learning(12 h)
• Forecasting Using Regression Models(8 h)
• Elements of Robotics(12 h)
• Robotic Project(16 h)
• Bayes nets & HMM (8 h)

Prerequisites:high school algebra; some programming background is useful but not necessary and passion to robots.

Tutors of the stream are:

Mr. Dmitry Dziuba, MSc., researcher, AILEN lab; researcher, Institute of Mathematical Machines and Systems, Kyiv, Ukraine.

Mr. Dmytro Fishman, MSc., PhD student, Institute of Computer Science, University of Tartu, Estonia.

Dr. Mikhail Khokhlov, Ph.D., researcher, Computer Science Department of Moscow Institute of Physics and Technology; Yandex, Moscow, Russian Federation.

Ms. Anna Leontjeva, MSc., PhD student, Institute of Computer Science, University of Tartu, Estonia.

Mr. Oleksii Molchanovskyi, MSc., PhD student, Senior teacher, National Technikal University of Ukraine "Kyiv Polytechnic Institute", Kyiv, Ukraine.

Mr. Dmytro Prylipko, MSc., PhD student, Faculty of Electrical Engineering and Information Technology Institute for Electronics, Otto-von-Guericke University Magdeburg, Germany.

Mr. Konstantin Tretyakov, MSc., PhD student, Istitute of Computer Science, University of Tartu, Estonia.

Number of credits: 3 ECTS (P/F)