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Summer School on Evolutionary Algorithms and Machine Learning at NIT Jalandhar [Hybrid, June 16-20]: Register Now!

Gurjit Kaur

May 7, 2025
2 min read

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NIT Jalandhar is organising Summer School on Evolutionary Algorithms and Machine Learning on 16-20 June, 2025.

About the Summer School

The “Evolutionary Algorithms and Machine Learning: Theory and Applications” summer school offers a comprehensive and intensive exploration of the intersection between Evolutionary Algorithms (EAs) and Machine Learning (ML). This program is designed to provide participants with both theoretical foundations and practical insights into how evolutionary strategies can enhance machine learning models and vice versa. Participants will delve into the principles of evolutionary algorithms, including genetic algorithms, genetic programming, and other optimization techniques that draw inspiration from natural evolution.

The summer school will focus on fundamentals of evolutionary algorithm and machine learning algorithms. It will provide a platform to the learners to have a hands on experience on these methods along with theoretical foundations. The program is very suitable for the students, research scholars and faculty members.

The program covers how these algorithms can be used to solve complex optimization problems, optimize neural networks, or even evolve new machine learning models. Furthermore, the summer school incorporates hands-on sessions where attendees will apply these concepts in real-world scenarios, such as optimizing hyperparameters in ML models, designing adaptive learning systems, or tackling large-scale computational problems.

Leading experts in the field will share their research and experiences, offering valuable perspectives on both the challenges and breakthroughs in this rapidly evolving domain. The summer school aims to foster a collaborative learning environment, where participants can engage in discussions, work on projects, and gain insights into the latest trends and future directions of research at the intersection of evolutionary algorithms and machine learning.