AI with MATLAB – From First Steps to a Bitcoin Volatility Forecast Problem

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In this webinar, an Opti-Num Solutions/MathWorks team will introduce you to Machine Learning (ML) with MATLAB – a computing platform widely used by engineers and scientists. We will apply a variety of machine learning techniques, with a focus on applying an ensemble of regression trees, to illustrate forecasting the 10-day volatility of Bitcoin. This illustration will demonstrate the simplicity of implementing a MATLAB workflow to solve a complete machine learning problem, consisting of data acquisition, preprocessing, model building and validation. Additionally, the facilitators will share sites to access learning tools for MATLAB programming and Machine Learning.

The speakers are:
Ms. Hazel Chieza, a graduate engineer at Opti-Num Solutions. She has completed various software engineering projects such as automated and KPI reporting using MATLAB. Additionally, she provides technical support on MATLAB and Simulink Products. Hazel has published in the fields of Optimisation and Quantum Computing. She is completing her MSc in Computational and Applied Mathematics at the University of the
Witwatersrand

Dr. Marco Rossi, EDU Customer Success Engineer at MathWorks. He supports academics and researchers in the use of MATLAB and Simulink for their research and teaching activities. He holds a doctorate in Mechanical Engineering from the Dresden University of Technology, Germany. His research interests are theoretical modeling and numerical simulation (finite elements) of soft materials, such as polymers and smart gels.
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Crypto Trading
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