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25 lines
1.4 KiB
Markdown
25 lines
1.4 KiB
Markdown
# MIT18.330 : Introduction to numerical analysis
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## Descriptions
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- Offered by: MIT
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- Prerequisites: Calculus, Linear Algebra, Probability theory
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- Programming Languages: Julia
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- Difficulty: 🌟🌟🌟🌟🌟
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- Class Hour: 150 hours
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While the computational power of computers has been helping people to push boundaries of science, there is a natural barrier between the discrete nature of computers and this continuous world, and how to use discrete representations to estimate and approximate those mathematically continuous concepts is an important theme in numerical analysis.
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This course will explore various numerical analysis methods in the areas of floating-point representation, equation solving, linear algebra, calculus, and differential equations, allowing you to understand (1) how to design estimation (2) how to estimate errors (3) how to implement algorithms in Julia. There are also plenty of programming assignments to practice these ideas.
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The designers of this course have also written an open source textbook for this course (see the link below) with plenty of Julia examples.
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## Course Resources
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- Course Website: <https://github.com/mitmath/18330>
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- Textbook: <https://fncbook.github.io/fnc/frontmatter.html>
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- Assignments: 10 problem sets
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## Personal Resources
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All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPic/MIT18.330 - GitHub](https://github.com/PKUFlyingPig/MIT18.330)
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