MATLAB Seminar at Texas A&M University: Data Analysis, Machine Learning, Code Optimization and Acceleration
Date: june 7, 2017
Data Analysis and Machine Learning using MATLAB: 1:00 p.m. - 2:30 p.m.
Optimizating and Accelerating MATLABE Code: 2:45 p.m. - 4:15 p.m.
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OverviewPlease join Texas A&M University High Performance Research Computing (HPRC) and the MathWorks on June 7th in Interdisciplinary Life Sciences Building (ILSB).
Session 1: Data Analytics and Machine Learning using MATLABUsing Data Analytics to turn large volumes of complex data into actionable information can help you improve engineering design and decision-making processes. However, developing effective analytics and integrating them into business systems can be challenging. In this seminar, you will learn approaches and techniques available in MATLAB to tackle these challenges. Using machine learning techniques, you will see how you can manage raw data, identify key features that impact your model, train multiple models, and perform model assessments.
- Accessing, exploring, and analyzing data stored in files, the web, and data warehouses
- Techniques for cleaning, exploring, visualizing, and combining complex multivariate data sets
- Prototyping, testing, and refining predictive models using machine learning methods
- Integrating and running analytics within enterprise business systems and interactive web applications
Session 2: Optimizing and Accelerating MATLAB CodeIn this session, we will discuss and demonstrate simple ways to improve and optimize your code that can boost execution speed by orders of magnitude. We will also address common pitfalls in writing MATLAB code, explore the use of the MATLAB Profiler to find bottlenecks, introduce our parallel computing tools to solve computationally and data-intensive problems on multicore computers and clusters, and finally talk about tools to automatically translate your MATLAB code into C.
- Optimizing MATLAB code to boost execution speed
- Automatically generating portable C code from MATLAB
- Employing multi-core processors and GPUs to speed up your computations
- Scaling up to computer clusters, grid environments or clouds