A Review of Machine Learning Techniques for Anomaly Detection - Dr. David Green

This course provides a review of machine learning techniques for anomaly detection. The learning outcomes include understanding traditional decomposition, point anomalies, contextual anomalies, and collective anomalies. Students will learn about deep neural networks, two styles of explanation, training a neural network, hierarchical classification, supervised and unsupervised learning, traditional clustering, time series type analysis, spectral clustering, false positives, challenges, risks, and applications in IT infrastructure security and smart cities. The teaching method involves theoretical explanations and practical examples. The course is intended for individuals interested in data science, artificial intelligence, and machine learning techniques for anomaly detection.