Our Events

In this webinar, you will learn how to empower your robots using deep learning and reinforcement learning for perception and motion control in autonomous robotics applications including robot manipulators, autonomous mobile robots, and UAVs using MATLAB.

Deep learning and machine learning techniques can be applied to a range of radar and wireless applications to meet the increased level of system requirements of operating in a crowded RF spectrum.

MATLAB provides flexible, two-way integration with many programming languages, including Python. This allows different teams to work together and use MATLAB algorithms within production software and IT systems. This webinar will cover how to call MATLAB from Python and how to call Python libraries from MATLAB.

In this webinar, we will demonstrate a real-world medical application of an ECG analysis and show how you can use MATLAB & Simulink to train and verify AI models. You will learn some basic and advanced techniques in order to fully utilize the benefits of machine learning in biomedical signal analysis.

This 1 hr webinar provides a technical overview on security challenges in the aerospace and defence domain. The seminar will cover state-of-the-art methods for the mitigation of security threats. We will highlight the strengths of model-based design to attain security across different stages of product life-cycle management. We will also relate to important requirements of the DO-326 set.

In this webinar, you will see the workflow of artificial intelligence that can be implement in the industry. The workflow spans from data preparation to the deployment of the algorithm in real life application.

In this presentation, Heng Chen from HSBC takes a deep technical dive into extreme value theory and parametric modelling techniques to estimate Value at Risk at high quantiles.

In this webinar, MathWorks and Hydro-Québec will discuss how modeling and simulation support the development of microgrid systems that contain renewable energy and energy storage. Through a worked example of a representative grid-connected microgrid, both grid-forming and grid-following operation will be considered.

Conceptually, a good model is free of biases and robust in the face of uncertainty. In reality, executing a model risk management framework can be complex to manage people, processes, and systems effectively. Learn practical techniques on how to include AI and machine learning algorithms into your model risk management framework.