18.065_lecture_notes

Lecture notes of "Matrix Methods in Data Analysis, Signal Processing, and Machine Learning"

Course: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning | Mathematics | MIT OpenCourseWareby Gilbert Strang http://t.cn/EKdMLqN

Videos: https://www.bilibili.com/video/av53055190

• Lecture 1: The Column Space of A Contains All Vect
• Lecture 2: Multiplying and Factoring Matrices
• Lecture 3: Orthonormal Columns in Q Give Q^TQ=I
• Lecture 4: Eigenvalues and Eigenvectors
• Lecture 5: Positive Definite and Semidefinite Matrices
• Lecture 6: Singular Value Decomposition (SVD)
• Lecture 7: Eckart-Young: The Closest Rank k Matrices to A
• Lecture 8: Norms of Vectors and Matrices
• Lecture 9: Four Ways to Solve Least Square Problems
• Lecture 10: Survey of Difficulties with Ax=b
• Lecture 11: Minimizing ||x|| Subject to Ax=b
• Lecture 12: Computing Eigenvalues and Singular Values
• Lecture 13: Randomized Matrix Multiplication
• Lecture 14: Low Rank Changes in A and Its Inverse