In this course students will be exposed to the applications of artificial intelligence in metallurgical processes. Students will have 20 hours of theory and 20 hours of practical and the course will include a refresher of R and Python programming, understanding data sets, learning statistical methods, learning machine learning techniques, understanding where and when to use a specific machine learning technique, and also understanding the limitations of artificial intelligence and common misconceptions and fallacious statistical interpretations. The students will also be exposed to four real industrial case studies from ferrous, nonferrous, and light metals industries. Here students will learn on 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 helped improve the process. The course will also include four practical sessions for hands-on training in analyzing data sets from metals industries using R & Python.