Robotics and Machine Learning
The goal of the course is to give the theoretical and practical skills in reinforcement learning applications, e.g. robotics and target tracking. In such applications, we try to determine some property of a system or some other quantity based on noise-corrupted measurements from sensors. The course will motivate the need for various filtering techniques such as Kalman filters and particle filters using a number concrete examples taken from robotics.
Probabilistic Robotics, Bayesian filtering, Simultaneous Localization, and Mapping.
Basics of Probability Theory, Basic knowledge of Python, R or another programming language.
Dr. Tetiana Bogodorova
Research Associate, Faculty of Applied Sciences, Ukrainian Catholic University, Lviv, Ukraine
Tetiana Bogodorova received the Ph.D. degree in Electrical Systems, School of Electrical Engineering, KTH – Royal Institute of Technology, Stockholm in 2017. She received the B.S. degree in Computerized Systems, Automatics and Control and the M.Sc. degree in Automatic and Control Systems specialized in control theory from the National Technical University of Ukraine – Kyiv Polytechnic Institute. Her experience includes the development of the operations support system for telecommunication industry as a System Engineer with the System Analytics Group, Research and Development, NetCracker Technology Corporation. Her current research interests lie on the intersection of machine learning and systems analysis, modeling, and validation.
Fields of interests: Reinforcement Learning, Systems Analysis, Machine Learning, Mathematical Modeling, Model Validation.