The course focuses on machine learning and the applications of machine learning approaches to anomaly detection and outlier detection. The techniques range from traditional statistical machine learning methods (such as one-class support vector machines, tree based learning algorithms) to state-of-the-art approaches (such as deep learning, unsupervised and semi-supervised learning methods). During the course, students will have chances to apply the gained knowledge to solving various real-world problems from financial time series analysis to cyber security, Video surveillance system, and industrial damage detection.
Computer vision. Machine Learning. Deep Learning.
Received the Ph.D. degree in Computer Science from Dorodnitsyn Computing Centre of Russian Academy of Sciences (CCRAS), 2013. Lecturer at Computer Science Department, School of Information and Communication Technology (SoICT), Hanoi University of Science and Technology (HUST).