Online Interactive Courses

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 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 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

This course focuses on importing and preparing data for data analytics applications. It is intended for data scientists or domain experts who need to automate the processing, analysis, and visualization of data from multiple sources. Topics include: 

• Importing data
• Processing data
• Customizing visualizations
• Working with irregular data

This two-hour course provides a quick introduction to ODEs and their numerical solutions. It covers MATLAB techniques to solve first-order ODEs and systems of first-order ODEs numerically. Topics include:

• MATLAB ODE solvers
• Writing ODE functions
• General solution structures
• Solving systems of ODEs

This 1.5-hour course provides an introduction to matrix methods in MATLAB for systems of linear equations and eigenvalue decomposition. Topics include:

• Solving systems of linear equations
• Overdetermined and underdetermined systems
• Calculating eigenvalues and eigenvectors

This two-hour course introduces descriptive statistics and data fitting using MATLAB. It covers methods for data exploration, interpolation, and curve fitting. Topics include:

• Statistical visualizations
• Normal and uniform probability distributions
• Fitting and evaluating regression models
• Linear and nonlinear interpolation

This two-hour course provides an introduction to root-finding methods for solving nonlinear equations. Topics include:

• Visualizing roots
• The bisection method
• Solving nonlinear equations using fzero
• Solving systems of nonlinear equations using fsolve

This two-day course provides hands-on experience using the features in the MATLAB^® language to write efficient, robust, and well-organized code. These concepts form the foundation for writing full applications, developing algorithms, and extending built-in MATLAB capabilities. Details of performance optimization, as well as tools for writing, debugging, and profiling code are covered. Topics include:

• Utilizing development tools
• Verifying application behavior
• Creating robust applications
• Structuring code
• Structuring data
• Managing data efficiently
• Creating a toolbox

This three-day course provides a comprehensive introduction to the MATLAB^® technical computing environment for financial professionals. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course, with an emphasis on practical application to finance, such as time-series analysis, Monte Carlo simulation, portfolio management, and empirical modeling. Topics include:

• Working with the MATLAB user interface
• Importing data from spreadsheets and other data sources
• Representing financial data in MATLAB
• Working with dates and times
• Visualizing data and results using advanced plots and graphics
• Filtering large datasets based on logical criteria
• Automating tasks using code files
• Algorithm development using programming constructs
• Performing data analysis, modeling, and simulation
• Generating reports and exporting results

This two-hour course provides an introduction to symbolic math operations using MATLAB and Symbolic Math Toolbox. Topics include:

• Algebraic manipulation and simplification
• Solving symbolic equations
• Units of measurement
• Calculus and differential equations