Machine Learning

Internet security: machine learning is the new changing force of general science, and the most important game-changer in security. The dynamic nature of attacks, malware, digital fraud and adversaries can only be matched by complex and accurate machine learning systems. These systems take advantage of our data, game strategies, algorithms and expertise in real attacks to fulfil a broad range of security solutions for both companies and the civil society stratosphere.org

Game theory: We study combination of machine learning and game theory in two main directions. First, we use machine learning in computing strategies for large complex games. Machine learning is used to automatically abstract the most important features of the games and to compute heuristics that improve scalability of our game theoretic algorithms. Second, we use game theory to understand machine learning in adversarial settings. Existing machine learning techniques are very vulnerable to carefully crafted adversarial samples. Network intrusion detection can be easily avoided by modifying attack patterns, visual traffic sign recognition can be confused by small innocent-looking stickers, face recognition can be misled by specially crafted glasses. We believe that game theory is the right framework to study this phenomenon http://aic.fel.cvut.cz/gametheory/

Robotics: In the Computational Robotics Laboratory (ComRob), we are seeking for unique solutions to address real-world challenges to improve quality of life and to understand principles emerging in nature. We are solving problems at the intersections of the artificial intelligence and autonomous robotic systems using traditional computational approaches, but also machine learning techniques. In addition to statistical methods and supervised learning applied, e.g., in terrain classification, locomotion control, and spatiotemporal mapping; we are also working on semi-supervised and unsupervised learning methods in planning and signal processing. Our mission is to develop lifelong learning robotic system that will adapt to environmental changes, improve its performance by incremental learning in long-term autonomous tasks in previously unknown environments that can be populated by humans https://comrob.fel.cvut.cz/

Close