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 event-driven simulation
week 9
Lecture 16
-
Raytracing in 3D
Lecture 17
-
Raytracing live-coding
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 26 -- Discrete Fourier Transform
Airs on:
2020-12-08T14:35:00 EST
What is a Discrete Fourier Transform?
Closing Remarks
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