This course provides an introduction to artificial intelligence (AI) and machine learning (ML) methods with a focus on applications in manufacturing engineering. Students will gain both theoretical foundations and practical experience with key techniques, including data preparation, exploratory data analysis, data visualization, supervised and unsupervised learning, deep learning, reinforcement learning, and large language models. Emphasis will be placed on understanding how these methods can be leveraged to solve real-world challenges in manufacturing, such as process optimization, defect detection, prediction, and intelligent automation.
In addition to lectures, students will engage in hands-on exercises and a course project that allows them to apply the concepts learned. Projects may involve developing models, analyzing datasets, or exploring novel applications of AI/ML tools in engineering contexts. Students are encouraged to design a project that aligns with their interests and may choose to work independently or collaboratively in teams.
Prior knowledge in manufacturing science, materials processing, computer science, or data science is helpful but not required. Prior coding or programming experience is not required. Basic coding (for loops, functions, if/else statements) is helpful.