We will take applications such as climate change and show how you can participate in the big open source community looking to find solutions to challenging problems with exposure to github and parallel computing. |
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You will learn mathematical ideas by immersion into the mathematical process, performing experiments, seeing the connections, and seeing just how much fun math can be. |
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Our course material is built using real code, and instead of a book, we have a series of interactive notebooks. On our website, you can play with sliders, buttons and images to interact with our simulations. You can even go further, and modify and run any code on our website! |
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| In literature it’s not enough to just know the technicalities of grammar. In music it’s not enough to learn the scales. The goal is to communicate experiences and emotions. For a computer scientist, it’s not enough to write a working program, the program should be written with beautiful high level abstractions that speak to your audience. This class will show you how. |
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Check out our interactive lecture material on computationalthinking.mit.edu!
MIT's numbering scheme gone nuts: (1.C25/6.C25/12.C25/16.C25/18.C25/22.C25)
This course is part of the Common Ground.
Lectures: TBA
Prerequisites: 6.100A, 18.03, 18.06 or equivalents (meaning some programming, dif eqs, and lin alg)
Instructors: A. Edelman, more TBA
Teaching Assistants: TBA
Office Hours: TBA
Grading: TBA
Lecture Recordings: Available on Canvas under the Panopto Video tab. Should be published the evening after each lecture.
Links: Worth bookmarking.
| Piazza TBA | Canvas TBA | Julia | JuliaHub |
|---|---|---|---|
| Discussion | HW submission | Language | GPUs |
Focuses on algorithms and techniques for writing and using modern technical software in a job, lab, or research group environment that may consist of interdisciplinary teams, where performance may be critical, and where the software needs to be flexible and adaptable. Topics include automatic differentiation, matrix calculus, scientific machine learning, parallel and GPU computing, and performance optimization with introductory applications to climate science, economics, agent-based modeling, and other areas. Labs and projects focus on performant, readable, composable algorithms and software. Programming will be in Julia. Expects students have some familiarity with Python, Matlab, or R. No Julia experience necessary.
Counts as an elective for CEE students, an advanced subject (18.100 and higher) for Math students, an advanced elective for EECS students, and a computation restricted elective for NSE students. AeroAstro students can petition department to count this class as a professional subject in the computing area. (Professors may be open to petitioning for counting for other programs.)
Class is appropriate for those who enjoy math and wish to see math being used in modern contexts.
While not exactly the same as our past Computational Thinking Class... not entirely different either.
- Making mathematics your playground: By the end of the course, students will be able to write interactive Julia code that aids in their understanding of new mathematical systems or concepts.
- Abstractions: By the end of the course students will be able to use the unique abstractions that exist in the Julia language to write code that can be part of a huge ecosystem. (By contrast many "one-off" homeworks in traditional courses are not able, by their very nature, to reveal the value of abstraction.)
- Open Source and group collaborations: By the end of the course students will be able to participate in a larger open source project and also will have experienced how programming language can help break down barriers between areas making real the dream of scientific bilinguals that has been promoted at MIT. (See for example former MIT President Reif in the NYT back in 2018.)