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Course Outline
Introduction
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
Requirements
- An understanding of Mathematica
Audience
- Data Scientists
14 Hours