Holiday
DELTA台達102 T3R3R4
This course aims to provide an introduction of various machine learning techniques to detect anomalies in data for several different kinds of applications. The course will cover three types of anomaly detection problems, time-series signal, image, and video anomaly detections. We will start with introducing traditional machine learning techniques for anomaly detection, and the latter part of the course will focus on deep learning approaches to anomaly detection. We will discuss the recent advances of deep learning techniques for anomaly detection. This course will focus on the application of anomaly detection in smart manufacturing, video surveillance, and cybersecurity.
Course keywords: Anomaly Detection, Anomaly Localization, Machine Learning, Deep Learning, Deep Neural Network, Outlier Detection 一、課程說明 (Course Description) This course aims to provide an introduction of various machine learning techniques to detect anomalies in data for several different kinds of applications. The course will cover three types of anomaly detection problems, time-series signal, image, and video anomaly detections. We will start with introducing traditional machine learning techniques for anomaly detection, and the latter part of the course will focus on deep learning approaches to anomaly detection. We will discuss the recent advances of deep learning techniques for anomaly detection. This course will focus on the application of anomaly detection in smart manufacturing, video surveillance, and cybersecurity. 二、指定用書 (Text Books) 1. Anomaly Detection Principles and Algorithms, Kishan G. Mehrotra, Chilukuri K. Mohan, and HuaMing Huang, Springer Publisher, 2017 2. some recent papers assigned for reading 3. Lecture slides 三、教學進度 (Syllabus) 1. Introduction to Anomaly Detection 2. Distance-based Anomaly Detection 3. Clustering-based Anomaly Detection 4. Model-based Anomaly Detection 5. Anomaly Detection for Time-Series Data 6. Introduction to Deep Learning 7. Practical DNN training techniques 8. Generative Models 9. Deep Learning for Image Anomaly Detection 10. Deep Learning for Video Anomaly Detection 11. Deep Learning for Time-Series Anomaly Detection 12. Zero-shot and Few-shot Anomaly Detection 13. Guest lectures 14. Final Project Presentation 四、教學方式 (Teaching Method) Lectures (instructors and guest speakers) and interactive discussion 五、參考書籍 (References) Beginning Anomaly Detection Using Python-based Deep Learning: With Keras and PyTorch, Sridhar Alla and Suman Kalyan Adari, 2019 六、成績考核 (Evaluation) Homeworks 30% Midterm Exam 30% Term project (team-based) 30% Class Attendance 5% Class Participation 5% 七、可連結之網頁位址: eeclass: https://eeclass.nthu.edu.tw/
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Average GPA 3.87
Std. Deviation 0.96
16週課程。
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