Introduction to Machine Learning and Reinforcement Learning for Precision Engineers

Dr. Amir Barati Farimani (Carnegie Mellon University)

In this entry-level tutorial, we will explore machine learning techniques with the goal of identifying suitable opportunities to use them in problems related to precision engineering and control engineering. The course assumes no past experience with these techniques and begins with a review of the terminology and differences and similarities between machine learning, reinforcement learning, deep learning, and artificial intelligence tools. With the nomenclature demystified, we will next focus on identifying problems for which these tools can provide a benefit. Some hands-on tutorials with Jupyter Notebooks will be overviewed and practiced.  In the second part of the tutorial, we will cover reinforcement learning basics with some classical control examples such as Cart pole, Inverted Pendulum, etc. An introduction to Open AI Gym environment will be given. Finally, we will review case studies of interest to the precision engineers and control with more focus on RL.


Professor Farimani joined the Department of Mechanical Engineering at Carnegie Mellon University in the fall of 2018. He was previously a postdoctoral fellow at Stanford University. He received his PhD in Mechanical Engineering in 2015. His lab at CMU focuses on the problems at the interface of Mechanical Engineering, data science and machine learning. His lab uses the state of the art deep learning and machine learning algorithms and tools to learn, infer and predict the physical phenomena pertinent to mechanical engineering. Currently, he is teaching AI and ML to a large class of graduate students at CMU. He received the Stanley I. Weiss best thesis award from the University of Illinois in 2016 and was recognized as an Outstanding Graduate Student in 2015. During his post-doctoral fellowship at Stanford, Dr. Barati Farimani has developed data-driven, deep learning techniques for inferring, modeling, and simulating the physics of transport phenomena and for materials discovery for energy harvesting applications. He taught multiple workshops on machine learning and artificial intelligence for both industry and academia such as  Autodesk ( and precision engineering society.