Understand the computational tools used in engineering problem-solving in this 2-course program.
- Simulate physical processes using numerical discretization methods.
- Assess cost-accuracy trade-offs in numerical simulation.
- Learn powerful optimization techniques and understand their fundamental role in machine learning.
- Describe canonical machine learning problems from a statistical perspective.
- Practice real-world forecasting and risk assessment problems using Monte Carlo simulation.
- Understand why and how machine learning methods may improve engineering problem-solving
- Learn how researchers make better predictions with missing or sparse data
- Transfer machine learning approaches developed in one industry to another industry
- Quantify risk and clarify salient features from data in complex systems
- Assess conditions when a machine learning approach may not be helpful or worth the extra effort