• 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