About

Main Objectives

• Understand AI/ML fundamentals and their potential applications.

• Learn and apply a wide range of ML techniques for both regression and classification-based problems.

• Learn sound methodologies and steps in developing reliable ML models, including data preprocessing, outlier detection, feature engineering, hyperparameter optimization, model training, and model evaluation.

• Gain extensive hands-on experience through practical examples and projects.

• Understand and apply PINN to integrate physical laws with ML for solving complex engineering problems.

• Equip participants with the skills to implement ML-based solutions for complex engineering problems, focusing on practical, real-world applications.

Expected Outcomes

• Participants will have a strong understanding of ML fundamentals and their practical applications.

• Participants will be proficient in implementing a wide range of ML techniques.

• Participants will develop expertise in sound methodology for building reliable ML models.

• Participants will gain hands-on experience through practical examples and projects.

• Participants will understand the basics of PINN, which integrates physical laws with ML.

Training Format

This comprehensive training program will be offered in two formats:

• Online: Participants can attend sessions virtually through Zoom.

• In-Person: Participants may join us at the Center for Advanced Energy Studies (CAES) Auditorium for in-person sessions.

Google Classroom Integration 

Google Classroom will be used to enhance interaction and provide a comprehensive learning experience. Participants will have access to training materials, updates, and announcements. The platform will also support discussions, exercises, and the capstone project as well as offer office hours for additional support.