Executive Development Programme in Building Anomaly Detection Systems
-- ViewingNowThe Executive Development Programme in Building Anomaly Detection Systems is a certificate course designed to equip learners with essential skills in identifying, mitigating, and preventing anomalies in various systems and processes. With the rapid increase in cyber attacks and system failures, there is a growing industry demand for professionals who can build and maintain robust anomaly detection systems.
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⢠Introduction to Anomaly Detection Systems: Understanding the basics of anomaly detection, its importance, and applications in building and infrastructure management.
⢠Data Acquisition and Preprocessing: Collecting, cleaning, and transforming data from various sources to prepare for anomaly detection analysis.
⢠Feature Engineering and Selection: Techniques for extracting relevant features and selecting the most important ones for building effective anomaly detection models.
⢠Machine Learning Algorithms for Anomaly Detection: Overview of popular machine learning algorithms used in anomaly detection, including statistical, unsupervised, and supervised methods.
⢠Deep Learning for Anomaly Detection: Exploring the use of deep learning models for anomaly detection, including autoencoders, convolutional neural networks, and recurrent neural networks.
⢠Performance Evaluation Metrics: Understanding the key metrics used to evaluate the performance of anomaly detection models, including precision, recall, F1 score, and receiver operating characteristic (ROC) curve.
⢠Real-time Anomaly Detection Systems: Techniques for building real-time anomaly detection systems, including stream processing and online learning algorithms.
⢠Domain-specific Applications of Anomaly Detection Systems: Case studies and applications of anomaly detection systems in building and infrastructure management, including predictive maintenance, energy efficiency, and fault detection.
⢠Ethical and Legal Considerations: Discussion of the ethical and legal considerations surrounding the use of anomaly detection systems, including data privacy, bias, and accountability.
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