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Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)

(34 Lectures Available)

S# Lecture Course Institute Instructor Discipline
1
  • Lecture 10: Survey of Difficulties with Ax = b (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
2
  • Lecture 11: Minimizing ‖x‖ Subject to Ax = b (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT 53 Basic and Health Sciences
3
  • Lecture 12: Computing Eigenvalues and Singular Values (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
4
  • Lecture 13: Randomized Matrix Multiplication (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
5
  • Lecture 14: Low Rank Changes in A and Its Inverse (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
6
  • Lecture 15: Matrices A(t) Depending on t, Derivative = dA/dt (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
7
  • Lecture 16: Derivatives of Inverse and Singular Values (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
8
  • Lecture 17: Rapidly Decreasing Singular Values (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Alex Townsend Basic and Health Sciences
9
  • Lecture 18: Counting Parameters in SVD, LU, QR, Saddle Points (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
10
  • Lecture 19: Saddle Points Continued, Maxmin Principle (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
11
  • Lecture 1: The Column Space of A Contains All Vectors Ax (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
12
  • Lecture 20: Definitions and Inequalities (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
13
  • Lecture 21: Minimizing a Function Step by Step (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
14
  • Lecture 22: Gradient Descent: Downhill to a Minimum (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
15
  • Lecture 23: Accelerating Gradient Descent (Use Momentum) (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
16
  • Lecture 24: Linear Programming and Two-Person Games (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
17
  • Lecture 25: Stochastic Gradient Descent (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
18
  • Lecture 26: Structure of Neural Nets for Deep Learning (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
19
  • Lecture 27: Backpropagation: Find Partial Derivatives (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
20
  • Lecture 2: Multiplying and Factoring Matrices (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
21
  • Lecture 30: Completing a Rank-One Matrix, Circulants! (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
22
  • Lecture 31: Eigenvectors of Circulant Matrices: Fourier Matrix (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
23
  • Lecture 32: ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
24
  • Lecture 33: Neural Nets and the Learning Function (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences
25
  • Lecture 34: Distance Matrices, Procrustes Problem (M-I-T)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T) MIT Prof. Dr. Gilbert Strang Basic and Health Sciences

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