AI & Autonomous Industrial Systems

About

Explore the cutting-edge science and technologies driving the transformation of industrial production towards greater efficiency and sustainability.

The AI & Autonomous Industrial Systems Summer School offers a unique opportunity to delve into the transformative role of artificial intelligence and machine learning in advancing industrial systems toward autonomy, efficiency, and sustainability. In an era defined by rapid technological advancements, understanding the core principles of data-driven decision-making and intelligent system design is essential for tackling the challenges of modern industrial processes.

The remarkable progress in AI and machine learning over the past decades has fundamentally reshaped industries, from manufacturing and energy systems to healthcare and transportation. With growing demands for efficiency, resilience, and environmental responsibility, the integration of intelligent systems into industrial processes is no longer a vision of the future—it is a necessity.

This summer school programme has been meticulously designed to align with the cutting-edge research and educational expertise of the Autonomous Industrial Systems Lab (AISL) at Imperial College London. The programme will equip participants with the foundational skills and hands-on experience required to address real-world industrial challenges through intelligent, data-driven approaches.

This intensive two-week programme is open to undergraduate and early postgraduate students across a range of disciplines who are keen to explore the science and technologies underpinning autonomous systems. Participants will gain exposure to the fundamentals of AI and machine learning, engage with real-world industrial problems, and collaborate on projects that simulate the challenges faced by today’s industries.

The optional add on third and final week is presented by the Department of Chemical Engineering. On the final day of the course participants will celebrate their Finale dinner together with attendees on the Carbon Capture Theory and Pilot Plant Operation course.

Course leader

Dr Mehmet Mercangöz

Target group

Students currently studying an undergraduate or postgraduate degree

Course aim

Introduction to Statistics and Linear Algebra: Building the mathematical foundation for data science and machine learning.

Machine Learning Fundamentals: Exploring the key concepts and techniques that drive AI applications.

Optimization Fundamentals: Understanding the role of optimization in decision-making and intelligent system design.

Supervised and Unsupervised Learning: Applying models to tasks such as prediction, classification, and clustering.

Active Learning: Advanced topics in Bayesian Optimization and Reinforcement Learning for decision-making under uncertainty.

Project-Based Learning: Participants will apply their knowledge through engaging, hands-on projects

Fee info