From b07087b0680824a971c8a6b52e7bf989234b8a4c Mon Sep 17 00:00:00 2001 From: <> Date: Thu, 27 Feb 2025 07:19:14 +0000 Subject: [PATCH] Deployed a82f0307 with MkDocs version: 1.6.1 --- en/数学进阶/numerical/index.html | 2 +- search/search_index.json | 2 +- sitemap.xml | 486 +++++++++++++++---------------- sitemap.xml.gz | Bin 5038 -> 5038 bytes 数学进阶/numerical/index.html | 2 +- 5 files changed, 246 insertions(+), 246 deletions(-) diff --git a/en/数学进阶/numerical/index.html b/en/数学进阶/numerical/index.html index 6358a454..3b35473b 100644 --- a/en/数学进阶/numerical/index.html +++ b/en/数学进阶/numerical/index.html @@ -1,4 +1,4 @@ - MIT18.330: Introduction to numerical analysis - csdiy.wiki
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MIT18.330 : Introduction to numerical analysis

Descriptions

  • Offered by: MIT
  • Prerequisites: Calculus, Linear Algebra, Probability theory
  • Programming Languages: Julia
  • Difficulty: 🌟🌟🌟🌟🌟
  • Class Hour: 150 hours

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.

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.

The designers of this course have also written an open source textbook for this course (see the link below) with plenty of Julia examples.

Course Resources

Personal Resources

All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/MIT18.330 - GitHub

MIT18.330 : Introduction to numerical analysis

Descriptions

  • Offered by: MIT
  • Prerequisites: Calculus, Linear Algebra, Probability theory
  • Programming Languages: Julia
  • Difficulty: 🌟🌟🌟🌟🌟
  • Class Hour: 150 hours

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.

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.

The designers of this course have also written an open source textbook for this course (see the link below) with plenty of Julia examples.

Course Resources

Personal Resources

All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPic/MIT18.330 - GitHub

MIT18.330 : Introduction to numerical analysis

课程简介

  • 所属大学：MIT
  • 先修要求：微积分，线性代数，概率论
  • 编程语言：Julia
  • 课程难度：🌟🌟🌟🌟🌟
  • 预计学时：150 小时

计算机强大的计算能力帮助人们在科学领域不断突破边界，不过计算机的离散本质和这个连续的世界有着天然鸿沟，而如何用离散的表示去估计和逼近那些数学上连续的概念，则是数值分析的重要主题。

这门课会在浮点表示、方程求解、线性代数、微积分、微分方程等领域探讨各类数值分析方法，让你在 Julia 的编程实践中反复体悟（1）如何建立估计（2）如何估计误差（3）如何用算法实现估计 这一系列步骤。

这门课的设计者还编写了配套的开源教材（参见下方链接），里面有丰富的 Julia 实例。

课程资源

资源汇总

@PKUFlyingPig 在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/MIT18.330 - GitHub 中。

MIT18.330 : Introduction to numerical analysis

课程简介

  • 所属大学：MIT
  • 先修要求：微积分，线性代数，概率论
  • 编程语言：Julia
  • 课程难度：🌟🌟🌟🌟🌟
  • 预计学时：150 小时

计算机强大的计算能力帮助人们在科学领域不断突破边界，不过计算机的离散本质和这个连续的世界有着天然鸿沟，而如何用离散的表示去估计和逼近那些数学上连续的概念，则是数值分析的重要主题。

这门课会在浮点表示、方程求解、线性代数、微积分、微分方程等领域探讨各类数值分析方法，让你在 Julia 的编程实践中反复体悟（1）如何建立估计（2）如何估计误差（3）如何用算法实现估计 这一系列步骤。

这门课的设计者还编写了配套的开源教材（参见下方链接），里面有丰富的 Julia 实例。

课程资源

资源汇总

@PKUFlyingPig 在学习这门课中用到的所有资源和作业实现都汇总在 PKUFlyingPig/MIT18.330 - GitHub 中。