MIT线性代数中文笔记

今天推荐的资料是MIT线性代数中文笔记[1]，对应的课程是[2]。中文笔记和课程链接如下所示：

第1课：方程组的几何解释

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B01%5D%E6%96%B9%E7%A8%8B%E7%BB%84%E7%9A%84%E5%87%A0%E4%BD%95%E8%A7%A3%E9%87%8A/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0%5B%E4%B8%80%5D.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V29E773

第2课：矩阵消元

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B02%5D%E7%9F%A9%E9%98%B5%E6%B6%88%E5%85%83/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0%5B%E4%BA%8C%5D.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V29EGPP

第3课：乘法和逆矩阵

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B03%5D%E4%B9%98%E6%B3%95%E5%92%8C%E9%80%86%E7%9F%A9%E9%98%B5/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0%5B%E4%B8%89%5D.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V29FCHO

第4课：A的LU分解

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B04%5DA%20%E7%9A%84%20LU%20%E5%88%86%E8%A7%A3/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0%5B%E5%9B%9B%5D.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V29FCHO

第5课：转置-置换-向量空间R

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B05%5D%20%E8%BD%AC%E7%BD%AE-%E8%BD%AC%E6%8D%A2-%E5%90%91%E9%87%8F%E7%A9%BA%E9%97%B4%20R/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0%5B%E4%BA%94%5D.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V29FRJK

第6课：列空间和零空间

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B06%5D%20%E5%88%97%E7%A9%BA%E9%97%B4%E5%92%8C%E9%9B%B6%E7%A9%BA%E9%97%B4/%E5%88%97%E7%A9%BA%E9%97%B4%E5%92%8C%E9%9B%B6%E7%A9%BA%E9%97%B4.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V29FRJK

第7课：求解Ax=0：主变量、特解

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B07%5D%20%E6%B1%82%E8%A7%A3%20Ax%20%3D%200%EF%BC%8C%E4%B8%BB%E5%8F%98%E9%87%8F%EF%BC%8C%E7%89%B9%E8%A7%A3/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0%5B%E4%B8%83%5D.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V29FRJK

第8课：求解Ax=b：可解性和解的结构

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B08%5D%20Ax%3Db%20%E7%9A%84%E5%8F%AF%E8%A7%A3%E6%80%A7%E5%92%8C%E8%A7%A3%E7%9A%84%E7%BB%93%E6%9E%84/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B08.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2ABHV8

第9课：线性相关性、基、维数

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B09%5D%20%E7%BA%BF%E6%80%A7%E7%9B%B8%E5%85%B3%E6%80%A7%EF%BC%8C%E5%9F%BA%EF%BC%8C%E7%BB%B4%E6%95%B0/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B09.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2ACDCT

第10课：四个基本子空间

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B10%5D%20%E5%9B%9B%E4%B8%AA%E5%9F%BA%E6%9C%AC%E5%AD%90%E7%A9%BA%E9%97%B4/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B010.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2ADFDT

第11课：矩阵空间、秩1矩阵和小世界图

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B11%5D%20%E7%9F%A9%E9%98%B5%E7%A9%BA%E9%97%B4%20%E7%A7%A91%20%E7%9F%A9%E9%98%B5/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B011.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AJS2T

第12课：图和网络

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B12%5D%20%E7%9F%A9%E9%98%B5%E5%BA%94%E7%94%A8%EF%BC%9A%E5%9B%BE%E4%B8%8E%E7%BD%91%E7%BB%9C/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B012.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AIUTE

第13课：复习一

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B13%5D%20%E5%A4%8D%E4%B9%A0%E4%B8%80/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B013.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AJ69K

第14课：正交向量与子空间

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B14%5D%20%E6%AD%A3%E4%BA%A4%E5%90%91%E9%87%8F%E4%B8%8E%E5%AD%90%E7%A9%BA%E9%97%B4/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B014.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AJEMH

第15课：子空间投影

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B15%5D%20%E5%AD%90%E7%A9%BA%E9%97%B4%E6%8A%95%E5%BD%B1/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B015.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AJLJU

第16课：投影矩阵和最小二乘

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B16%5D%20%E6%8A%95%E5%BD%B1%E7%9F%A9%E9%98%B5%E5%92%8C%E6%9C%80%E5%B0%8F%E4%BA%8C%E4%B9%98/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B016.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AOJPU

第17课：正交矩阵和Gram-Schmidt正交化

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B17%5D%20%E6%AD%A3%E4%BA%A4%E7%9F%A9%E9%98%B5%E5%92%8CGram-Schmidt%20%E6%AD%A3%E4%BA%A4%E5%8C%96/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B017.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AORLS

第18课：行列式及其性质

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B18%2C19%5D%20%E8%A1%8C%E5%88%97%E5%BC%8F%E4%BB%8B%E7%BB%8D/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B018%2C19.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AP150

第19课：行列式公式和代数余子式

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B18%2C19%5D%20%E8%A1%8C%E5%88%97%E5%BC%8F%E4%BB%8B%E7%BB%8D/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B018%2C19.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AP150

第20课：克拉默法则、逆矩阵、体积

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B20%5D%20%E5%85%8B%E8%8E%B1%E5%A7%86%E6%B3%95%E5%88%99%E3%80%81%E9%80%86%E7%9F%A9%E9%98%B5%E3%80%81%E4%BD%93%E7%A7%AF/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B020.pdf
课程链接：http://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AQ40C

第21课：特征值和特征向量

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B21%5D%20%E7%89%B9%E5%BE%81%E5%80%BC%E5%92%8C%E7%89%B9%E5%BE%81%E5%90%91%E9%87%8F/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B021.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AV2R8

第22课：对角化和A的幂

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B22%5D%20%E5%AF%B9%E8%A7%92%E5%8C%96%E5%92%8C%20A%20%E7%9A%84%E5%B9%82/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B022.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AV6GL

第23课：微分方程和exp(At)

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B23%5D%20%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E5%92%8Cexp(At)/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B023.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AVIBM

第24课：马尔可夫矩阵;傅立叶级数

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B24%5D%20%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E7%9F%A9%E9%98%B5%3B.%E5%82%85%E7%AB%8B%E5%8F%B6%E7%BA%A7%E6%95%B0/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B024.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AVOAV

第25课：复习二

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B25%5D%20%E5%A4%8D%E4%B9%A0%E4%BA%8C/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B025.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M7E4C9V6P

第26课：对称矩阵及正定性

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B26%5D%20%E5%AF%B9%E7%A7%B0%E7%9F%A9%E9%98%B5%E5%8F%8A%E6%AD%A3%E5%AE%9A%E6%80%A7/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B026.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2AVUL4

第27课：复数矩阵和快速傅里叶变换

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B27%5D%20%E5%A4%8D%E6%95%B0%E7%9F%A9%E9%98%B5%E5%92%8C%E5%BF%AB%E9%80%9F%E5%82%85%E9%87%8C%E5%8F%B6%E5%8F%98%E6%8D%A2/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B027.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2B4U77

第28课：正定矩阵和最小值

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B28%5D%20%E6%AD%A3%E5%AE%9A%E7%9F%A9%E9%98%B5%E5%92%8C%E6%9C%80%E5%B0%8F%E5%80%BC/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B028.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2B5J3P

第29课：相似矩阵和若尔当形

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B29%5D%20%E7%9B%B8%E4%BC%BC%E7%9F%A9%E9%98%B5%E5%92%8C%E8%8B%A5%E5%B0%94%E5%BD%93%E5%BD%A2/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B029.pdf
课程链接：http://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2B5OKE

第30课：奇异值分解

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B30%5D%20%E5%A5%87%E5%BC%82%E5%80%BC%E5%88%86%E8%A7%A3/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B030.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2B5R1G

第31课：线性变换及对应矩阵

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B31%5D%20%E7%BA%BF%E6%80%A7%E5%8F%98%E6%8D%A2%E5%8F%8A%E5%AF%B9%E5%BA%94%E7%9F%A9%E9%98%B5/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B031.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2B60PJ

第32课：基变换和图像压缩

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B32%5D%20%E5%9F%BA%E5%8F%98%E6%8D%A2%E5%92%8C%E5%9B%BE%E5%83%8F%E5%8E%8B%E7%BC%A9/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B032.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2BA04Q

第33课：单元检测3复习

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B33%5D%20%E5%8D%95%E5%85%83%E6%A3%80%E6%B5%8B3%E5%A4%8D%E4%B9%A0/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B033.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2BA6N8

第34课：左右逆和伪逆

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B34%5D%20%E5%B7%A6%E5%8F%B3%E9%80%86%E5%92%8C%E4%BC%AA%E9%80%86/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B034.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2BADQ9

第35课：期末复习

笔记链接：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes/blob/master/%5B35%5D%20%E6%9C%9F%E6%9C%AB%E5%A4%8D%E4%B9%A0/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B035.pdf
课程链接：https://open.163.com/newview/movie/free?pid=M6V0BQC4M&mid=M6V2BAN1H

参考文献：
[1]MIT-Linear-Algebra-Notes：https://github.com/ML-NLPChina/MIT-Linear-Algebra-Notes
[2]麻省理工公开课：线性代数：https://open.163.com/newview/movie/courseintro?newurl=%2Fspecial%2Fopencourse%2Fdaishu.html

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