Description: Bayesian networks are one of many models that are useful for predictive modeling. This article introduces the concept of Bayesian network and shows how time-series data can be modeled for a Bayesian network.

Description: A discussion on supervised learning--the class of machine learning where a model is trained to produce a specific result for a given input.

Description: Evaluates the ability of various model types to automatically approximate various mathematical formulas. This can be used as a guide for feature engineering.

Description: This article discussed the NEAT family of neural network algorithms. NEAT automatically determines the number of hidden neurons, as well as their structure. Additionally HyperNEAT can scale to very large problem domains.

Description: This article shows how to deal with very large CSV files and the R programming language.

Description: Deep learning is a method that allows neural networks to be constructed with more than just one or two hidden layers. These layers can then be trained independently and simultaneously of each other. This article provides an introduction to deep belief neural networks.