Introduction to Machine Learning for Simulation Engineers and Scientists

Course Objective:

Data Analytics provides the technology to build data-driven predictive models and to search for interesting patterns in large amounts of data. At the core of data analytics lays the field of Machine Learning, which provides all the conceptual infrastructure and algorithms to build computer systems that learn from experience. Machine Learning is a subfield of Artificial Intelligence; it has received unprecedented attention lately due to its use in many real-world applications.
The course will explain how to build systems that learn and adapt borrowing from examples in industry and science, e.g.

• learning to predict medical diagnoses,
• anticipating machine failures,
• minimizing the cost of expensive simulations

The class will be self-contained (no previous knowledge will be assumed) other than basic University level Statistics and some coding experience (e.g. Python). Main topics include:

• neural networks
• deep learning
• decision trees
• unsupervised learning
• ensemble methods
• application of ML to FEA

Target Audience:

• This course is recommended for everyone interested in machine learning and its application in science and engineering.

Duration: 2 Days

Date: Oct 31 – Nov 1, 2024
Dec 12 – Dec 13, 2024

Time: 9 AM – 5 PM (CST)

Location: Online or In-person

If you are interested in this course, please send us an email to [email protected]