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.
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]
Instructor: Dr. Ricardo Vilalta
Dr. Ricardo Vilalta is a professor in the Center for Science, Technology, Engineering, and Mathematics at the University of Austin. He holds a Master's and Ph.D. in computer science from the University of Illinois at Urbana-Champaign. His research focuses on machine learning, statistical theory, artificial intelligence, and astroinformatics. He has received several awards, including a Fulbright scholarship, the Invention Achievement Award from IBM, the Best Paper Award at the European Conference on Machine Learning, and the CAREER Award from the National Science Foundation. Before UATX, Dr. Vilalta was a professor of computer science at the University of Houston and a researcher at the IBM T.J. Watson Research Center in New York. Dr. Vilalta has published over a hundred papers on AI, machine learning, and its applications.
Instructor: Dr. Arindam Chakraborty, PE
Dr. Arindam Chakraborty serves as the CTO of Engineering Consulting at VIAS3D. He holds PhD in Mechanical Engineering from University of Iowa. His doctoral research topics included stochastic modeling for FEA framework. Dr. Chakraborty has more than 15 years of simulation consulting experience in various industries such as Energy, Life Science, Consumer Goods, Hi-Tech. He has experience in automating complex engineering problems using codes in FORTRAN/C/C#/VBA/Java/Python. Dr. Chakraborty has more than 30 conference and journal publications, including invited talks at industry conferences and academia. He is currently focused on identifying industry applications where solving physics-based problems can benefit from a phased deployment of AI-ML-based tools to improve efficiency while maintaining product quality and safety.
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
- neural networks
- deep learning
- decision trees
- unsupervised learning
- ensemble methods
- application of ML to FEA
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]
Instructor: Dr. Ricardo Vilalta
Dr. Ricardo Vilalta is a professor in the Center for Science, Technology, Engineering, and Mathematics at the University of Austin. He holds a Master's and Ph.D. in computer science from the University of Illinois at Urbana-Champaign. His research focuses on machine learning, statistical theory, artificial intelligence, and astroinformatics. He has received several awards, including a Fulbright scholarship, the Invention Achievement Award from IBM, the Best Paper Award at the European Conference on Machine Learning, and the CAREER Award from the National Science Foundation. Before UATX, Dr. Vilalta was a professor of computer science at the University of Houston and a researcher at the IBM T.J. Watson Research Center in New York. Dr. Vilalta has published over a hundred papers on AI, machine learning, and its applications.
Instructor: Dr. Arindam Chakraborty, PE
Dr. Arindam Chakraborty serves as the CTO of Engineering Consulting at VIAS3D. He holds PhD in Mechanical Engineering from University of Iowa. His doctoral research topics included stochastic modeling for FEA framework. Dr. Chakraborty has more than 15 years of simulation consulting experience in various industries such as Energy, Life Science, Consumer Goods, Hi-Tech. He has experience in automating complex engineering problems using codes in FORTRAN/C/C#/VBA/Java/Python. Dr. Chakraborty has more than 30 conference and journal publications, including invited talks at industry conferences and academia. He is currently focused on identifying industry applications where solving physics-based problems can benefit from a phased deployment of AI-ML-based tools to improve efficiency while maintaining product quality and safety.
This course is available on request. If you are interested in this course, please send us an email to [email protected]