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Machine Learning for Anomaly Detection

Instructor

Dr. Dinh Viet Sang

Review

(0 Review)

Duration

50 Hours

Topics

08 Articles

Resources

25 Files

Access

Lifetime

Language

English

Certification

Yes

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.

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.



Instructor

Dr. Dinh Viet Sang

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).

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