MATLAB and Simulink Seminars

MATLAB Academic Tour 2018

Location Venue Start Date End Date
Torino, Italy
Università degli Studi di Torino 6 Dec 2018 - 09:30 6 Dec 2018 - 12:30


During this event, we will show the main capabilities available in MATLAB to address scientific computing, data science, and engineering design. You will learn how to use machine learning techniques in MATLAB to quickly explore your data, evaluate machine learning algorithms, compare the results, and apply the best machine learning approach for your problem. Moreover, we’ll describe the latest MATLAB features to address deep learning for object classification, feature extraction, clustering, image recognition, etc.

The second part of the presentation will be held by Arduino Engineers! They will demonstrate – via MATLAB, Simulink and the Arduino Engineering Kit - how to face important engineering challenges, like controls, system modeling, robotics and others, through the design, simulation and implementation of practical projects.

Who Should Attend

Professors, researchers and students are welcome to attend this event. Registration required.

About the Presenter

Stefano Olivieri

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’s Degree.




  • How to get the software
  • Additional tools for learning and teaching (MATLAB Online, MATLAB Mobile, MATLAB Grader)
  • Self-paced online courses and MATLAB Certification

Data Science with MATLAB

  • Training, evaluating and comparing of machine learning models
  • Differences between machine learning and deep learning
  • Import, use and manipulate pre-trained models such as GoogLeNet and ResNet
  • Import models from other frameworks, like TensorFlow, Keras and Caffe
  • Perform classification tasks on images
  • Speed up network training and inference with parallel computing and GPUs

Engineering design with MATLAB, Simulink and Arduino

  • Building and simulating engineering models like image processing, controls, and robotics
  • Working with Arduino sensors and components (microcontrollers, motors, encoders, etc.)
  • Automatic code generation for embedding algorithms onto hardware

Product Focus

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