Problems
Machine Learning for Mechanical Engineering
Preface
Foundational Skills
1
Reviewing Supervised Linear Models
2
Evaluating Machine Learning Models
3
Introduction to Gradient Descent
4
Review of Linear Unsupervised Learning
5
Taking Derivatives with Automatic Differentiation
6
Measuring Distribution Distances
7
Introduction to Inference
Model-Specific Approaches
8
Review of Neural Networks
9
Introduction to Push-Forward Generative Models – Generative Adversarial Networks (GANs)
10
GAN Training Pitfalls
11
Optimal Transport for Generative Models
References
Problems
12
Problem Set 1
In-Class Notebooks
13
Housing Price Data Visualization In-Class Exercise
Appendices
A
Helpful Tooling for Working with and Debugging Machine Learning Models
B
Course Lecture Progression
C
Review of Matrices and the Singular Value Decomposition
Problems
Here are the problem sets
References
12
Problem Set 1