Solving science and engineering problems with machine learning
Machine Learning and Related Techniques: The Why, What and How
David Meintrup, Professor at the Ingolstadt University of Applied Sciences, works as a statistical trainer and consultant for the semiconductor, solar, pharmaceutical and biotech industries, in addition to his teaching and research areas. He presents on the why, what and how of machine learning and related techniques for scientists and engineers, including terms and definitions, examples and pitfalls.
In this talk, he debunks such myths about machine learning and artificial intelligence (AI) as:
- We live in the era of AI.
- Deep learning means deep understanding.
- Deep neural nets are models of our brains.
- Deep learning networks are incredibly smart.
- Big data + AI = solution.
Rolls-Royce, Tata Steel and SABIC innovate with machine learning and AI
Definitions of machine learning and AI vary, depending on who you ask. And some of the claims are sensational: machine learning and AI are “dark magic” that will soon automate our jobs away. Really? No. In many cases, these buzzwords are just new names for tried-and-true programs, approaches and techniques, but adapted to take advantage of greater data availability.
With examples from engineering, petrochemical and manufacturing industries, this webinar provides a balanced assessment of what is possible when scientists and engineers solve problems with advanced statistical methods.
You’ll hear from:
- David Meintrup, Ingolstadt University of Applied Sciences
- Bernard Ennis, Tata Steel
- Steve King, Rolls-Royce
- Teena Bonizzi, SABIC