Overview
Advance your career with AI-driven engineering solutions. Gain practical skills to detect anomalies early and ensure system performance.
Syllabus
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- Introduction to Anomaly Detection
-- Definition and significance of anomaly detection
-- Overview of applications in engineering and industry
-- Types of anomalies: point, contextual, and collective
- Machine Learning Fundamentals
-- Supervised vs unsupervised learning
-- Overview of classification and clustering techniques
-- Evaluation metrics: precision, recall, F1 score, and ROC-AUC
- Data Preprocessing for Anomaly Detection
-- Data collection and data types
-- Data cleaning and handling missing values
-- Feature selection and dimensionality reduction
-- Normalization and standardization
- Unsupervised Methods for Anomaly Detection
-- Clustering-based approaches: k-means, DBSCAN
-- Density-based methods: Isolation Forest, Local Outlier Factor
-- Autoencoders for anomaly detection
- Supervised Methods for Anomaly Detection
-- Choosing the right labels for anomaly detection
-- Classification techniques for anomaly detection
-- Time-series anomaly detection
- Real-Time Anomaly Detection
-- Streaming data and continuous monitoring
-- Implementing real-time anomaly detection systems
-- Performance considerations in real-time systems
- Deploying Anomaly Detection Models
-- Model deployment strategies
-- Integrating anomaly detection in maintenance and monitoring workflows
-- Challenges and considerations in deployment
- Case Studies and Applications
-- Industrial manufacturing and predictive maintenance
-- Financial fraud detection
-- Network security and intrusion detection
- Tools and Platforms for Anomaly Detection
-- Overview of popular libraries and tools: Scikit-learn, TensorFlow, PyTorch
-- Cloud-based solutions and services
-- Building custom solutions with open-source tools
- Final Project
-- Real-world anomaly detection project
-- Dataset selection and problem definition
-- Building, evaluating, and presenting the anomaly detection model
Taught by
Megan Thompson, Kathy Tao, Rohit Ramanathan, Marissa D'Alonzo and Brian Buechel
Tags
On-Demand
