This course focuses on data analytics and machine learning techniques in MATLAB. It demonstrates how to use of unsupervised learning to discover features in large data sets, and it shows how to use supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results. Topics include:

• Organizing and preprocessing data
• Clustering data
• Creating classification and regression models
• Interpreting and evaluating models
• Simplifying data sets
• Using ensembles to improve model performance

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. The course explores themes of data analysis, visualization, modeling, and programming. Topics include:

• Working with the MATLAB user interface
• Entering commands and creating variables
• Analyzing vectors and matrices
• Visualizing vector and matrix data
• Working with data files
• Working with data types
• Automating commands with scripts
• Writing programs with branching and loops
• Writing functions

This course provides a comprehensive introduction to practical deep learning using MATLAB. It covers creating, training, and evaluating different kinds of deep neural networks. Topics include:

• Importing image and sequence data
• Using convolutional neural networks for image classification, regression, and object detection
• Using long short-term memory networks for sequence classification and forecasting
• Modifying common network architectures to solve custom problems
• Improving performance of a network by modifying training option