MATLAB Seminar at Texas A&M University: Data Analysis, Visualization, and Machine Learning
Date: March 2, 2017
Session 1: 1 p.m. - 2:30 p.m.
Session 2: 3 p.m. - 4:30 p.m.
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OverviewPlease join Texas A&M University High Performance Research Computing (HPRC) and the MathWorks on March 2nd in Rudder Tower, Room 301.
Session 1 – MATLAB for Data Analysis and Visualization:Is the analysis and visualization of your data manual, repetitive and time consuming? Does your analysis involve stitching tasks across multiple tools? Are you looking for ways to streamline and automate your analysis in a single environment? If the answer to any of these questions is “yes”, then come along to this free introductory seminar to learn how MATLAB can help you automate and streamline your data analysis workflows.
MATLAB is a high-level language and interactive platform for data analysis and visualization. Through examples, you will see how MATLAB enables you to quickly gain insight into your data, test hypothesis and explore ideas, and document and share your results.
- Access data from files and Excel spreadsheets
- Use interactive tools for iterative exploration and visualization Automate and capture your analysis via scripts and apps
- Share your results with others using one-click report generation
- Share analysis tools as standalone applications
Session 2 – Machine Learning with MATLAB:Engineers and data scientists work with large amounts of data in a variety of formats such as sensor, image, video, telemetry, databases, and more. They use machine learning to find patterns in data and to build models that predict future outcomes based on historical data.
In this session we explore the fundamentals of machine learning using MATLAB. We introduce machine learning techniques available in MATLAB to quickly explore your data, evaluate machine learning algorithms, compare the results and apply the best technique to your problem.
- Training, evaluating and comparing a range of machine learning models
- Using refinement and reduction techniques to create models that best capture the predictive power of your data
- Running predictive models in parallel using multiple processors to expedite your results
- Deploying your models in a variety of formats