MIT 18.S191/6.S083/22.S092  Fall 2020
Introduction to Computational Thinking
by
Alan Edelman
,
David P. Sanders
,
Grant Sanderson
, &
James Schloss
,
Benoit Forget
Course home
Software installation
Cheatsheets
Homework 0
Course content
week 1
Lecture 1

Images and Abstraction
Lecture 2

Convolutions
Homework 1
week 2
Lecture 3

Dynamic programming, Views and GPUs
Lecture 4

Live coding seam carving
Homework 2
week 3
Lecture 5

Structure and Dispatch
Lecture 6

Understanding data
Homework 3
week 4
Lecture 7

Introspection, COVID Data visualization
Lecture 8

Probability via computation
week 5
Lecture 9

Computational thinking, graphs are matrices, the expression problem
Lecture 10

Graphs and disease spread
Homework 4
week 6
Lecture 11

Epidemic modeling & Documenting code
Lecture 12

Macroscopic models: discrete vs continuous
Homework 5
week 7
No class on 10/13 as per
MIT Calendar
Lecture 13

Graphs and Network Dynamics
Homework 6
week 8
Lecture 14

Raytracing, your own parallelism, abstract arrays
Lecture 15

Billiard model and eventdriven simulation
week 9
Lecture 16

Raytracing in 3D
Lecture 17

Raytracing livecoding
Homework 7
week 10
Lecture 18

Hierarchical Thinking, Greedy Algorithms, Jacobi's Method, and Multigrid
Lecture 19
 Floating Point Arithmetic
Homework 8
week 11
Lecture 20

Introduction to Climate Modeling, Nonlinear dynamics and Stability
Lecture 21

Nonlinear Climate Dynamics and Snowball Earth
Homework 9
week 12
Lecture 22

Diffusion Equation and Time and Space evolution
Lecture 23

Heat Transfer by Ocean Currents
Homework 10
week 13
Lecture 24

Ocean Modeling, Generic Programming
Lecture 25

An overview of modern climate modeling
week 14
Lecture 26

Discrete Fourier Transform
Synchronize play when the class is live
Lecture 8  Probability via computation
Airs on:
20200924T14:35:00 EST
Segment 1: Probability via computation
Segment 2: MIT only live session
(see Canvas for details)
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