1. Probability Theory Basics
2. Cluster Analysis
3. ML Introduction with scikit-learn

Anomaly detection in machine learning is identifying unusual or anomalous data points in a dataset that differ significantly from the normal behavior or patterns of the rest of the data.

Anomalies can be caused by various factors, including measurement errors, data entry errors, equipment malfunctions, fraud or cyberattacks.

What is the goal of anomaly detection?

The goal of anomaly detection is to automatically identify and flag these unusual data points, which can help to detect and mitigate potential problems, improve the accuracy of predictive models, and enhance the overall performance and reliability of the system.

Anomaly detection techniques

There are several techniques used in anomaly detection, including:

1. Statistical methods: These involve analyzing the statistical properties of the dataset, such as the mean, variance, or other statistics, and identifying data points that fall outside a certain range;
2. Machine learning methods: These involve training a model on the normal behavior of the data and using it to identify deviations from this normal behavior. Examples include clustering, classification, and regression techniques;
3. Deep learning methods: These involve using neural networks to identify anomalies in large and complex datasets. Examples include autoencoders, recurrent neural networks (RNNs), and convolutional neural networks (CNNs);
4. Rule-based methods: These involve using predefined rules or thresholds to identify anomalies in the data.

Anomaly detection is used in various applications, including fraud detection, network intrusion detection, cybersecurity, medical diagnosis, and predictive maintenance in manufacturing and industrial settings.