Machine Learning with MATLAB
for Teaching & Research
Presenter Profile:
Gerardo Hernandez Correa holds a B.S in Physics from the University of Puerto Rico at Mayagüez and a M.S. in Applied Mathematics from the same institution. His area of research was the theory of distributions and inverse problems, in particular the identification of linear systems. In his master’s thesis “Identification of linear systems” Gerardo designed and implemented in MATLAB an, iterative, non-destructive method for retrieving the convolution kernel of linear systems.
Gerardo also holds a M.S in mechanical engineering from WPI and is currently completing the requirements for a PhD in mathematical sciences at the same institution. In his dissertation "An adaptive, multiresolution agent-based model of glioblastoma multiforme",Gerardo designed and implemented in MATLAB a multiresolution Agent-based model of the evolution of Brain tumors, in particular Glioblastoma multiforme.
His areas’ of interest include Numerical methods, in particular ODE and PDE solvers, Mathematical modeling,Dynamical systems and high performance computing, among others.
 SUTD Seminar 2018Photos from 8th May 2018. |  NUS Seminar 2018Photos from 9th May 2018. |
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 NTU Seminar 2018Photos from 9th May 2018. |
THANK YOU!
We have more than 700 attendees and it was full of wonderful and knowledgeable experience. We hope you gained valuable insights from the experts.
Part 1: (Data Analytics) 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.
Highlights include:
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Training, evaluating and comparing a range of machine learning models
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Using refinement and reduction techniques to create models that best capture the predictive power of your data
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Running predictive models in parallel using multiple processors to expedite your results
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Deploying your models in a variety of formats
Part 2: Teaching with MATLAB
One of the main goals of engineering and scientific education is to teach students how to deal with real-word problems. When developing their curricula, professors need to determine the right balancing between interesting and challenging topics so that students are encouraged to put into practice the theoretical concepts they have already learned.
The expectation from the industry is another key factor influencing the teaching. Nowadays, the young scientists and engineers need to be ready and proficient to proactively contribute to the modelling, design, and implementation processes.
During this presentation, we will give a general overview of the main capabilities available in MATLAB to inspire critical thinking and innovation as well as to prepare students for prominent careers in industry, where the tools are the de facto standard for R&D. Through practical examples, we will also describe how top universities worldwide are successfully integrating MATLAB into their curricula to make teaching more engaging and trigger interdisciplinary learning.
Presenter Profile:
Stefano received a Master’s Degree in Electrical Engineering at University of Bologna, Italy, in July 1995, and got a Post Graduate Advanced Degree in Information Technology at CEFRIEL, Polytechnic of Milan the same year.
He’s been with MathWorks since 2005. After spending eight years as a Senior Application Engineer in the field of Signal Processing and Communication Systems, supporting companies in the Communications, Electronics, Semiconductors and Aerospace and Defense industry segments, Stefano is currently working as a Customer Success Engineer to help the top universities with the adoption of MathWorks tools for effective teaching and research.
Before that, he worked with R&D labs in STMicroelectronics and Philips Research, were he dealt with the design and development of wireless communication and video processing systems.
Stefano has also been Contract Professor with the University of Milano for three years, where he was teaching Transmission Theory for the Telecommunication Software Engineering Bachelor Degree.
8th May Singapore University of Technology and Design (SUTD)
9th May National University of Singapore (NUS)
9th May Nanyang Technological University (NTU)
