/18.065_lecture_notes

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

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