About Course
Welcome! In this course, we will explore various applications of machine learning through a series of case studies.
Course Content
Introduction
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Welcome
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Common Course Themes
Case 1: Feature Engineering in Li-Ion Battery Life Prediction
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Introduction to Feature Engineering
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Predicting Lithium Ion Battery Lifetime
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Identifying Features
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Three Types of Regularization
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Applying Feature Engineering
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Why Feature Engineering and Elastic Net Matters
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Graded Assignment
Case 2: Machine Learning for Computational Imaging
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Machine Learning for Computational Imaging
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Inverse Problems
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Phase Retrieval
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Phase Extracting Neural Network
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Perceptual Loss
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Learning to Synthesize
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Tomography
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Computational Imaging Conclusion
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Graded Assignment
Case 3: Seismic Deepfakes: Neural Nets to Generate Missing Data
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Introduction to Seismic Waves
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Wave Equations
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Inversion
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From High to Low Frequencies
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Where Neural Networks Finally Come In
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Training and Testing
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Inversion Revisited
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Publications
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Graded Assignment
Case 4: Prediction of Oil and Gas Production
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Minimizing Decision-Making Risk
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Oil and Gas Leases: Predicting Future Production Rates
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Linear Regression and Predicting with Data
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Predicting Future Production Rates: Results
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Graded Assignment
Case 5: Machine Learning in Geometric Representations
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Introduction to Machine Learning from Geometric Representations
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Two Modalities of 3D Geometric Data
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Deep Learning from Point Clouds
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Applications of Point Cloud Learning
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Deep Learning from Vector Data
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Frontiers in 3D Learning
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Graded Assignment
Case 6: Quantifying Risk in Complex Systems Using Machine Learning
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Quantifying Risk of Extreme Events
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Probabilistic Description of Extreme Events
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Challenges Related to Extreme Events
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A Better Approach to Calculating Probability
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Active Learning and Optimal Experimental Design
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Using a New Output-Weighted Criterion
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Q Criterion Results
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Graded Assignment
Case 7: Machine Learning for Accelerating Computational Materials Discovery
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Introduction to Inorganic Chemistry
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The Machine Learning Tradeoff
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Representations for Transition Metals
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Training Set Performance and Details
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Feature Selection
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Uncertainty Quantification
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Accelerating Discovery
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Graded Assignment
Case 8: Practical Machine Learning in Composite Design
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What Is Materials Science and Engineering?
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Introducing Machine Learning to Materials Design
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Using Machine Learning for Image Classification
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Complementing Conventional Methods with Machine Learning
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Predicting Fracture Propagation
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Hype vs. Reality: Challenges for the Future
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Graded Assignment
Case 9: Machine Learning for Data Assimilation and Inverse Problems
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Inverse Problems from Energy to the Geosciences
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Uncertainty Quantification for Inverse Problems: The Bayesian Approach
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Computational Challenges of Bayesian Inversion
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Video from Finnos Oy
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Dimensionality Reduction
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Surrogate Modeling
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Data Assimilation: From Static to Sequential Data
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Nonlinear Ensemble Methods for Data Assimilation
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Applications of Machine Learning in the Aerospace Industry
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Predictive Health Monitoring of Aerospace System
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Graded Assignment
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Publications
Conclusion
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Conclusion: Re-Visiting Course Themes
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