[TRANSLATION] translate numerical.md (#246)

* Add files via upload

* Update numerical.md

* Update CS126.en.md

docs/数学进阶/CS126.en.md

@@ -18,7 +18,7 @@ In addition to the Homework, nine Labs will allow you to use probability theory
 
 ## Course Resources
 
-- Course Website: https://inst.eecs.berkeley.edu/~ee126/fa20/content.html
+- Course Website: <https://inst.eecs.berkeley.edu/~ee126/fa20/content.html>
 - Textbook: [PDF](https://link.springer.com/content/pdf/10.1007%2F978-3-030-49995-2.pdf), [Epub](https://link.springer.com/download/epub/10.1007%2F978-3-030-49995-2.epub), [Jupyter Notebook](https://jeanwalrand.github.io/PeecsJB/intro.html)
 - Assignments: refer to the course website.
 

docs/数学进阶/numerical.en.md

@@ -0,0 +1,25 @@
+# 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
+
+- Course Website: <https://github.com/mitmath/18330>
+- Textbook: <https://fncbook.github.io/fnc/frontmatter.html>
+- Assignments: 10 problem sets
+
+## Personal Resources
+
+All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPic/MIT18.330 - GitHub](https://github.com/PKUFlyingPig/MIT18.330)

docs/数学进阶/numerical.md

@@ -10,7 +10,7 @@
 
 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、线性代数、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。