Description

About the course

This course aimed at providing practical methods for incorporating Simulink in the classroom to enhance teaching of technical concepts. Engineering education involves a fine balance between teaching theory and imparting practical problemsolving skills. Educators are also challenged to provide real-world examples that enable students to appreciate how the theory being taught in class can be applied in industry. The use of Simulink models in class and throughout a course exposes students to a tool that is widely used in industry to design and model complex systems. At the same time, large-scale models can be used to illustrate how theoretical concepts relate to the bigger picture and how they can be applied to solve real-world problems.

In this course, it was demonstrated how Data analytics, signal processing and control systems models can help to bridge the gap between theory and application, thus providing extra motivation for students. The speaker demonstrated how using interactive models in class can help to address the different learning styles of students, allowing them to learn more actively. A session was planned at the end to review resources that can help teachers incorporate MATLAB & Simulink throughout a course.

Target Audience

The course can be taken by:

Students:All students who are pursuing any technical / professional courses related to computer science / Information Technology.

Teachers/Faculties: All computer science teachers/faculties who wish to acquire new skills.

Professionals: All technical professionals, who wish to upgrade their skills.

Course Features

• 24X7 Access: You can view lectures as per your own convenience.
• Online lectures: ~7 hours of online lectures with high-quality videos.
• Updated Quality content: Content is latest and gets updated regularly to meet the current industry demands.

Topics to be covered

• Lecture 1
  • MATLAB basics for budding engineer
  • Experimentation and Modeling in MATLAB
  • Design and Implementation
• Lecture 2
  • Project Based Learning (Arduino, Raspberry Pi)
  • Machine Learning and Data Analytics
• Lecture 3
  • Machine Learning and Data Analytics - Accessing, exploring, analyzing, and visualizing data in MATLAB
  • Common Machine Learning tasks such as feature selection and feature transformation
  • Using the Classification learner app and functions in the statistics and Machine Learning toolbox to perform
  • What is IoT?
  • Electrical Engineering using Simscape (Physical Modeling)
  • System Identification and Neural Network Based System Modeling Techniques and Electrical Engineering using SimPowersystems Control System design and analysis
• Lecture 4
  • Mechanical engineering concept using Simscape (Physical Modeling)
  • Multi body dynamics simulation using sim mechanics
• Lecture 5
  • Import Cad models using GetMechanics App
  • Implement control on low cost hardware - Arduino Demo of Magnetic Levitation System