With the explosion of applications of analytics and machine learning in diverse areas (such as aircraft engine prognosis, construction machinery, battery materials for EV and energy storage, agriculture, drug discovery in medicine, sports, finance, social sciences and the advertising industry) statistical learning skills are in high demand.
Industry and institutions are awash with all types of data archived over many years. Examples include: sensor data, binary alarm data with operator actions (categorical data) to ‘navigate’ the process to operate at desired conditions; image data that can be used to identify promising materials and design strategies for developing next-generation batteries; satellite imagery data for use in the design of precision irrigation systems. The fusion of information from such disparate sources of data is the key step in devising strategies for smart analytics platforms for the next generation of tools.

The focus in this seminar will be a simple introduction of tools and techniques that help in the process of analyzing and discovering information from data from operating processes, materials science, physical laws, chemistry and biology. Overall, AI and ML have the potential to revolutionize various industries, including agriculture, battery development for energy storage, developing screening tools to identify diseases from stained slides and classify metal microstructures that lead to better welds and joins. By leveraging the power of these technologies, manufacturers and engineers can optimize their processes and achieve higher levels of efficiency, productivity, and quality leading to the design of more efficient, sustainable, and reliable systems. It is predicted that over the next decade we will experience more progress than in the past 100 years combined as technology reshapes health, material sciences, energy and transportation and a wide range of other industries.