Teaching Machine Learning Through Cricket: A Practical Engineering Education Approach

TL;DR

This paper introduces LearnML@Cricket, a 12-week curriculum that uses cricket analytics to teach complex machine learning concepts through practical, hands-on experience, aiming to improve student understanding and skills.

Contribution

It presents a novel, cricket-based teaching approach for machine learning that integrates real datasets and practical labs, enhancing engineering education.

Findings

Expected improvement in student understanding of ML concepts

Enhanced practical skills through hands-on learning

Potential for broader application in engineering education

Abstract

Teaching complex machine learning concepts such as reinforcement learning and Markov Decision Processes remains challenging in engineering education. Students often struggle to connect abstract mathematics to real-world applications. We present LearnML@Cricket, a 12-week curriculum that uses cricket analytics to teach these concepts through practical, hands-on examples. By mapping game scenarios directly to ML algorithms, students learn through doing rather than memorizing. Our curriculum includes coding laboratories, real datasets, and immediate application to engineering problems. We propose an empirical study to measure whether this approach improves both understanding and practical implementation skills compared to traditional teaching methods.