麻省理工开放课程：线性代数 Linear Algebra 英文字幕包/共34课更新完毕[MP4]

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资源信息： 中文名: 麻省理工开放课程：线性代数 英文名: Linear Algebra 资源格式: MP4 课程类型: 数学 学校: 麻省理工 MIT 主讲人: Prof. Gilbert Strang 版本: 英文字幕包/共34课更新完毕 发行日期: 2005年 地区: 美国 对白语言: 英语 概述:

课程介绍： 这个基础课程主要阐述矩阵理论和线性代数。主题 重点放在对在其他学科的有用的法则上面，包括方程组，向量空间，行列式，特征值，相似性，以及正定矩阵。 导师介绍

Gilbert Strang was an undergraduate at MIT and a Rhodes Scholar at Balliol College, Oxford. His Ph.D. was from UCLA and since then he has taught at MIT. He has been a Sloan Fellow and a Fairchild Scholar and is a Fellow of the American Academy of Arts and Sciences. He is a Professor of Mathematics at MIT and an Honorary Fellow of Balliol College. He was the President of SIAM during 1999 and 2000, and Chair of the Joint Policy Board for Mathematics. He received the von Neumann Medal of the US Association for Computational Mechanics, and the Henrici Prize for applied analysis. The first Su Buchin Prize from the International Congress of Industrial and Applied Mathematics, and the Haimo Prize from the Mathematical Association of America, were awarded for his contributions to teaching around the world. His home page is math.mit.edu/~gs/ and his video lectures on linear algebra and on computational science and engineering are on ocw.mit.edu (mathematics/18.06 and 18.085). 截图：

注： 1.我会把麻省所有的微积分课程发出来，大约200个视频，有需要的同学请关注。 2.课件：http://ocw.mit.edu/courses/mathematics/18-...terials/，因为官网速度下载较快，这里暂不提供。 3.个人翻译的标题,有啥错误还请慷慨指正. 目录: Lecture 01: The Geometry of Linear Equations Lecture 02: Elimination with Matrices Lecture 03: Multiplication and Inverse Matrices Lecture 04: Factorization into A = LU Lecture 05: Transposes, Permutations, Spaces R^n Lecture 06: Column Space and Nullspace Lecture 07: Solving Ax = 0: Pivot Variables, Special Solutions Lecture 08: Solving Ax = b: Row Reduced Form R Lecture 09: Independence, Basis, and Dimension Lecture 10: The Four Fundamental Subspaces Lecture 11: Matrix Spaces; Rank 1; Small World Graphs Lecture 12: Graphs, Networks, Incidence Matrices Lecture 13: Quiz 1 Review Lecture 14: Orthogonal Vectors and Subspaces Lecture 15: Projections onto Subspaces Lecture 16: Projection Matrices and Least Squares Lecture 17: Orthogonal Matrices and Gram-Schmidt Lecture 18: Properties of Determinants Lecture 19: Determinant Formulas and Cofactors Lecture 20: Cramer's Rule, Inverse Matrix, and Volume Lecture 21: Eigenvalues and Eigenvectors Lecture 22: Diagonalization and Powers of A Lecture 23: Differential Equations and exp(At) Lecture 24: Markov Matrices; Fourier Series Lecture 24b: Quiz 2 Review Lecture 25: Symmetric Matrices and Positive Definiteness Lecture 26: Complex Matrices; Fast Fourier Transform Lecture 27: Positive Definite Matrices and Minimae Lecture 28: Similar Matrices and Jordan Form Lecture 29: Singular Value Decomposition Lecture 30: Linear Transformations and Their Matrices Lecture 31: Change of Basis; Image Compression Lecture 32: Quiz 3 Review Lecture 33: Left and Right Inverses; Pseudoinverse Lecture 34: Final Course Review