We present 3 clustering algorithms which will help us detect anomalies: DBSCAN, Gaussian Mixture Models and K-means. These 3 algorithms are very popular and basic but have passed the test of time. All these algorithms have many variations which try to overcome some of the disadvantages of the original implementation.

17 Anomaly Detection: Clustering

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We introduce the concept of a perceptron as the basic component of a neural network. We talk about how important is to understand the concept of backpropagation applied to a single neuron.

20 Perceptron: Machine Learning Begins

We discuss Independent Component Analysis as one of the most popular and robust techniques to decompose mixed signals. ICA has important applications in audio processing, video, EEG and in many datasets, which present very high multicollinearity. 

19 ICA: Independent Component Analysis

Anomaly detection is not something recent, techniques have been around for decades. Control charts are graphs with solid mathematical and statistical foundations which monitor how a process changes over time. They implement control limits which automatically flag anomalies in a process in real-time. Depending on the problem at hand, control charts might be a better alternative to more sophisticated machine learning approaches for anomaly detection. 

16 Anomaly Detection: Control Charts

Adaboost is one of the classic machine learning algorithms. Just like Random Forest and XGBoost, Adaboost belongs to the ensemble models, in other words, it aggregates the results of simpler classifiers to make robust predictions. The main different of Adaboost is that it is an adaptive algorithm, which means that it learns from the misclassified instances of previous models, assigning more weights to those errors and focusing its attention on those instances in the next round.

17 Anomaly Detection: Clustering

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