Introduction to
Computational Thinking
Make mathematics your playground!
by
Alan Edelman
,
David P. Sanders
&
Charles E. Leiserson
welcome
Software installation
Class reviews
Class logistics
Cheatsheets
Previous semesters
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
Fall 2022
18.S191
/
6.S083
/
22.S092
Section 3.3
Why we can't predict the weather
Lecture Video