Systematic Design of Bandgap Reference Circuit with Emphasis on Self-bias Loop Dynamics

Abstract

Machine learning is a powerful data analytics technique that enables computers to learn from experience, much like humans and animals do. Rather than relying on predetermined equations, machine learning algorithms extract patterns and insights directly from data using computational methods. MATLAB offers a user-friendly environment that allows users to apply advanced machine learning techniques without requiring extensive coding skills or prior experience.

In this hands-on introductory workshop, participants will be introduced to the fundamentals of machine learning and its practical applications across various data types. The workshop will cover key concepts such as supervised learning, feature extraction, and hyperparameter tuning. Attendees will explore essential data pre-processing steps and utilize MATLAB’s robust visualization tools to better understand their datasets.

Participants will gain practical experience in building and evaluating machine learning models for classification and regression tasks involving diverse data formats, including signals, images, and text. The workshop will also address techniques for hyperparameter tuning and feature selection to optimize model performance. Additionally, interoperability with other platforms will be discussed, highlighting MATLAB’s flexibility in integrating with different tools and workflows.

Finally, the session will demonstrate how to deploy machine learning models developed in MATLAB, enabling participants to translate their analytical solutions into real-world applications. This workshop is ideal for those seeking a practical introduction to machine learning with MATLAB, regardless of their prior experience in the field.