This is documentation for Mathematica 5, which was
based on an earlier version of the Wolfram Language.
View current documentation (Version 11.2)

Documentation / Mathematica / Add-ons & Links / Wolfram Research Products / Wolfram Education Training /

Wolfram Education Training

M330: Neural Networks

This course presents the theory and practice of neural networks with Mathematica and focuses on the Neural Networks package. The features and capabilities of the package are demonstrated, and numerous examples and practical hands-on exercises are included. The material is presented as a sequence of five lectures, each one followed by a problem session to help students understand the material and to provide a focused and practical learning experience. The lectures cover the different kinds of neural networks and the types of problems for which neural networks are used. Basic theoretical concepts, illustrated with graphs, figures, and examples, are covered to support practical neural network training.

The course is designed primarily for people who want and need to estimate relations in data using Mathematica. Students typically have wide-ranging backgrounds and include engineers and professionals who work with all kinds of data, including technical, medical, and economic data.


Introduction: overview of neural network history and types of problems: function approximation, classification, data clustering, time series, and dynamic systems

Feedforward Neural Networks and Radial Basis Functions: learning--overlearning and initialization of neural networks

Theory and Background of Neural Networks: description of the inherited problems when functions are fitted to data, possibilities for handling these problems using neural networks, and practical aspects

Nonlinear Dynamic Black-Box Modeling: modeling of time series and dynamic systems using linear and nonlinear models

Classification and Clustering with Neural Networks: two classes, many classes, neural network classifiers and relations to other classifiers, the perceptron as classifier, nearest-neighbor classification, vector quantization, unsupervised methods, self-organizing maps, and the Hopfield network

All Wolfram Education Group offerings are "hands-on" interactive courses and are taught by certified instructors in a professional computer classroom environment.

For more information: