This coming Summer, MSE will offer a new graduate course, MSE 1063: Application of Artificial Intelligence in Process Metallurgy (SYNC – Online), taught by Prof. Kinnor Chattopadhyay.
In this course students will be exposed to the applications of artificial intelligence in metallurgical processes. The course will include 20 hours of theory and 20 hours of hands on training in analyzing data sets from metals industries using R & Python. The students will be introduced to four real industrial case studies from ferrous, nonferrous and light metals industries, learn how data was acquired, what KPIs are tracked, nature of the data, what statistical models and machine learning techniques were employed and finally, how machine learning tools helped improve the process.
Prof. Chattopadhyay tells us more about the course:
What will students learn from this course?
By the end of the semester they will be able to:
- Formulate metallurgical problems, select appropriate machine learning techniques and apply to metallurgical processes
- Understand data sets, learning statistical methods, learning machine learning techniques, understanding where and when to use a specific machine learning technique, limitations of artificial intelligence and common misconceptions & fallacious statistical interpretations.
What sort of machine learning approaches will the students be expose to during this course?
The students will also be exposed to machine learning approaches such as supervised and unsupervised learning; regression, decision trees, artificial neural networks and support vector machines. They will also be trained on programming these algorithms in python/R applying them for a set of case studies pertaining to process and extractive metallurgy. Examples will include, iron making, steel making, welding, quality control, other manufacturing processes.
What are the prerequisites for this course?
Previous experience in coding is not a requirement for the course. The required coding skills will be taught and additional learning resources will be provided.
What type of homework/projects will be involved?
The course project will focus on building predictive models from real industrial data sets and understanding how machine learning can help in an industrial manufacturing setting for process and chemical industries.