Module 1: Images, Transformations, Abstractions
1.1 Images as Data and Arrays
1.2 Abstraction
Homework 1: Images and Arrays
1.3 Automatic Differentiation
1.4 Transformations with Images
Homework 2: Convolutions
1.5 Transformations II: Composability, Linearity and Nonlinearity
1.6 The Newton Method
Homework 3: Structure and language
1.7 Dynamic Programming
1.8 Seam Carving
1.9 Taking Advantage of Structure
Homework 4: Dynamic programming
Module 2: Social Science & Data Science
2.1 Principal Component Analysis
2.2 Sampling and Random Variables
Homework 5: Structure
2.3 Modeling with Stochastic Simulation
Homework 6: Probability distributions
2.4 Random Variables as Types
2.5 Random Walks
2.6 Random Walks II
2.7 Discrete and Continuous
Homework 7: Epidemic modeling I
2.8 Linear Model, Data Science, & Simulations
2.9 Optimization
Homework 8: Epidemic modeling II
Module 3: Climate Science
3.1 Time stepping
Homework 9: Epidemic modeling III
3.2 ODEs and parameterized types
3.3 Why we can’t predict the weather
3.4 Our first climate model
3.5 GitHub & Open Source Software
3.6 Snowball Earth and hysteresis
3.7 Advection and diffusion in 1D
Homework 10: Climate modeling I
3.8 Resistors, stencils and climate models
3.9 Advection and diffusion in 2D
3.10 Climate Economics
3.11 Solving inverse problems
