References
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
Foundations of Generative Models
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
12
Variational Autoencoders (VAEs)
13
Normalizing Flows
14
From Discrete Transformations to Continuous Flows
15
From Continuous Flows to Score Matching
16
From Score Matching to Diffusion Models
17
Flow Matching
18
Latent Generative Models
19
Introduction to Deep Reinforcement Learning
References
Problems
20
Problem Set 1
21
Problem Set 2
In-Class Notebooks
22
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
D
Reviewing Mathematical and Computational Foundations for Machine Learning
References
19
Introduction to Deep Reinforcement Learning
Problems