Spring 2021 | MIT 18.S191/6.S083/22.S092

Introduction to Computational Thinking

Math from computation, math with computation

by Alan Edelman, David P. Sanders & Charles E. Leiserson

Welcome Class Reviews Class Logistics Homework Syllabus and videos Software installation Cheatsheets Previous semesters

Module 1: Images, Transformations, Abstractions

1.1 - Images as Data and Arrays 1.2 - Abstraction 1.3 - Automatic Differentiation 1.4 - Transformations with Images 1.5 - Transformations II: Composability, Linearity and Nonlinearity 1.6 - The Newton Method 1.7 - Dynamic Programming 1.8 - Seam Carving 1.9 - Taking Advantage of Structure

Module 2: Social Science & Data Science

2.1 - Principal Component Analysis 2.2 - Sampling and Random Variables 2.3 - Modeling with Stochastic Simulation 2.4 - Random Variables as Types 2.5 - Random Walks 2.6 - Random Walks II 2.7 - Discrete and Continuous 2.8 - Linear Model, Data Science, & Simulations 2.9 - Optimization

Module 3: Climate Science

3.1 - Time stepping 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 3.8 - Resistors, stencils and climate models 3.9 - Advection and diffusion in 2D 3.10 - Climate Economics 3.11 - Solving inverse problems

This is the Spring 2021 edition

For previous versions of this class, see:

Fall 2020
Spring 2020
Fall 2019

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