This course teaches robust deep learning methods for anomaly detection. The learning outcomes include understanding anomaly detection techniques such as spectral techniques, PCA, and matrix factorization. Students will learn to apply auto-encoders for anomaly detection and compare them with conventional methods. The course covers training robust auto-encoders and analyzing datasets for anomaly detection. The teaching method involves video lectures and experimental comparisons. The intended audience for this course includes data scientists, machine learning engineers, and researchers interested in anomaly detection using deep learning methods.
KDD 2020: Robust Deep Learning Methods for Anomaly Detection
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Intro
Anomaly Detection: Video Surveillance.
Anomaly Detection: By Spectral Techniques
Anomaly Detection: PCA
Conventional Anomaly Detection Techniques
Matrix Factorization Approach: PCA
Auto-encoders for anomaly detection.
Comparison: Conventional Anomaly Detection Methods
Robust (convolution) Auto-Encoders RCAE
RCAE Vs Robust PCA (1)
Training RCAE (1)
Summary of Datasets
Anomaly Detection: Methods Compared
Experiment Settings
Methodology
Non Inductive: Top anomalous Images Detected USPS : 220 images of '1's, and 11 images of 7 (anomalous)
Non Inductive Anomaly Detection: Performance
Image De-noising Capability: RCAE vs RPCA
Conclusion
Taught by
Association for Computing Machinery (ACM)
