Course Outline


What is AI?

  • Computational Psychology
  • Computational Philosophy

Machine Learning

  • Computational learning theory
  • Computer algorithms for computational experience

Deep Learning

  • Artificial neural networks
  • Deep learning vs. machine learning

Preparing the Development Environment

  • Installing and configuring Mathematica

Machine Learning

  • Importing and separating data
  • Normalizing and interpolating data
  • Grouping and sorting elements

Predictors and Classifiers

  • Working with a linear model
  • Representing a data set
  • Generating a sequence of values

Supervised Machine Learning

  • Implementing supervised tasks
  • Using the training data
  • Measuring performance
  • Identifying clusters

Summary and Conclusion


  • An understanding of Mathematica


  • Data Scientists
  14 Hours

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