Are AI techniques needed to be taught to students & research scholars of core engineering fields?

Now a days it is very common to use an AI for engineering problems. But are the recent graduate engineers ready to leverage the power of AI?

Are artificial intelligence methods like fuzzy logic and artificial neural networks needed to be taught to engineering students & research scholars of core branches like mechanical, electrical and civil engineering?

Why to teach AI in core Engineering Field?

If you’re wondering Why engineers should understand AI approaches, read on. How can I get AI skills? Or Where do I begin? Go on reading.

AI for Engineering

According to a report by one of the largest employability assessment companies, only 3% of engineers in India have new-age tech skills like Artificial Intelligence, Machine Learning & Data Science. It could be one of the main reasons for the unemployability of engineers in the new tech industry.

Today, practically all industrial sectors, from services to products, are using AI techniques. In the era of Industry 4.0, all businesses are attempting to apply artificial intelligence (AI) techniques in various applications to surpass the limitations of traditional mathematical approaches.

In addition to being utilized in cutting-edge technology like autonomous vehicles and bots, AI approaches have also become a crucial component of some of the most well-known products, like cameras and smartphones.

This situation necessitates that all engineers, regardless of their specialty, possess a fundamental understanding of AI approaches.

AI Courses for Engineering Students

To cope with this scenario, Rhyni – Tech skills & fundamentals provides two foundation courses on crucial AI for engineers & research scholars of engineering domains.

The first course is about Fuzzy Logic, whereas the second is about Artificial Neural Networks.

Fuzzy logic techniques map input-output by logical reasoning in a real-world language called linguistic variables.

On the other hand, Artificial Neural Network(ANN) maps input-output with the help of known data and training algorithms. Immense adaptation of the ANN techniques in various applications gave birth to branches like Machine Learning, Deep Learning, Data Science, and Big data.

The titles of the courses are “Fuzzy Logic: Quick Start Guide” and “Fundamentals of Artificial Neural Network with MATLAB”.

These courses are made to present each AI approach in the simplest possible terms and to provide engineers with hands-on practice in developing cutting-edge skills.

See complete description about each course at the link below.

Fuzzy Logic: Quick start GuideFundamentals of Artificial Neural Network with MATLAB

Students can start learning Fuzzy Logic and or Artificial Neural Network from scratch. Both course covers MATLAB simulation tools and MATLAB programming for the application of AI approaches.

Any of these course does not require knowledge of high end coding languages like C++, java, or python,

These course are specially designed for students of the core engineering fields.

It will be helpful to mechanical, electrical, civil, and electronics engineering students.

To start learning AI for engineering, visit above course links.

After engineering students, research scholars in the engineering field may benefit the most from these courses. For more information, I encourage research scholars to read the article “How did AI techniques help me during my doctoral study of electrical engineering”.

Online School of Electrical Engineering

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By Dr. Jignesh Makwana

Dr. Jignesh Makwana, Ph.D., is an Electrical Engineering expert with over 15 years of teaching experience in subjects such as power electronics, electric drives, and control systems. Formerly an associate professor and head of the Electrical Engineering Department at Marwadi University, he now serves as a product design and development consultant for firms specializing in electric drives and power electronics.