Section 1 : Data Structures for Linear Algebra

Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 2 What Linear Algebra Is 23:1
Lecture 3 Plotting a System of Linear Equations 9:18
Lecture 4 Linear Algebra Exercise 5:6
Lecture 5 Tensors 2:33
Lecture 6 Scalars 13:4
Lecture 7 Vectors and Vector Transposition 12:19
Lecture 8 Norms and Unit Vectors 14:37
Lecture 9 Basis, Orthogonal, and Orthonormal Vectors 4:30
Lecture 10 Matrix Tensors 8:24
Lecture 11 Generic Tensor Notation 6:43
Lecture 12 Exercises on Algebra Data Structures 2:8

Section 2 : Tensor Operations

Lecture 13 Segment Intro 1:19
Lecture 14 Tensor Transposition 3:52
Lecture 15 Basic Tensor Arithmetic, incl 6:12
Lecture 16 Tensor Reduction 3:31
Lecture 17 The Dot Product
Lecture 18 Exercises on Tensor Operations 2:38
Lecture 19 Solving Linear Systems with Substitution 9:47
Lecture 20 Solving Linear Systems with Elimination 11:47
Lecture 21 Visualizing Linear Systems 10:59

Section 3 : Matrix Properties

Lecture 22 Segment Intro 2:5
Lecture 23 The Frobenius Norm 5:1
Lecture 24 Matrix Multiplication 24:29
Lecture 25 Symmetric and Identity Matrices 4:41
Lecture 26 Matrix Multiplication Exercises 7:21
Lecture 27 Matrix Inversion 17:6
Lecture 28 Diagonal Matrices 3:25
Lecture 29 Orthogonal Matrices 5:16
Lecture 30 Orthogonal Matrix Exercises 15:0

Section 4 : Eigenvectors and Eigenvalues

Lecture 31 Segment Intro 17:53
Lecture 32 Applying Matrices 7:32
Lecture 33 Affine Transformations 18:20
Lecture 34 Eigenvectors and Eigenvalues 26:13
Lecture 35 Matrix Determinants 8:4
Lecture 36 Determinants of Larger Matrices 8:42
Lecture 37 Determinant Exercises 4:41
Lecture 38 Determinants and Eigenvalues 15:43
Lecture 39 Eigendecomposition
Lecture 40 Eigenvector and Eigenvalue Applications 12:29

Section 5 : Matrix Operations for Machine Learning

Lecture 41 Segment Intro 3:22
Lecture 42 Singular Value Decomposition 10:50
Lecture 43 Data Compression with SVD 11:0
Lecture 44 The Moore-Penrose Pseudoinverse 12:23
Lecture 45 Regression with the Pseudoinverse 18:24
Lecture 46 The Trace Operator 4:36
Lecture 47 Principal Component Analysis (PCA)
Lecture 48 About Certification Pdf

Section 6 : Limits

Lecture 49 Segment Intro 3:3
Lecture 50 Intro to Differential Calculus 13:25
Lecture 51 Intro to Integral Calculus 2:24
Lecture 52 The Method of Exhaustion 6:45
Lecture 53 Calculus of the Infinitesimals 9:34
Lecture 54 Calculus Applications 8:35
Lecture 55 Calculating Limits 17:49
Lecture 56 Exercises on Limits 6:7

Section 7 : Derivatives and Differentiation

Lecture 57 Segment Intro 1:16
Lecture 58 The Delta Method 15:47
Lecture 59 How Derivatives Arise from Limits 13:53
Lecture 60 Derivative Notation 4:20
Lecture 61 The Derivative of a Constant 1:28
Lecture 62 The Power Rule 1:16
Lecture 63 The Constant Multiple Rule 3:10
Lecture 64 The Sum Rule 2:26
Lecture 65 Exercises on Derivative Rules 11:8
Lecture 66 The Product Rule
Lecture 67 The Quotient Rule 4:4
Lecture 68 The Chain Rule 6:46
Lecture 69 Advanced Exercises on Derivative Rules 11:48
Lecture 70 The Power Rule on a Function Chain 4:37

Section 8 : Automatic Differentiation

Lecture 71 Segment Intro 1:49
Lecture 72 What Automatic Differentiation Is 4:43
Lecture 73 Autodiff with PyTorch 6:17
Lecture 74 Autodiff with TensorFlow 3:52
Lecture 75 The Line Equation as a Tensor Graph 19:41
Lecture 76 Machine Learning with Autodiff 40:11

Section 9 : Partial Derivative Calculus

Lecture 77 Segment Intro 22:38
Lecture 78 What Partial Derivatives Are 29:22
Lecture 79 Partial Derivative Exercises 6:15
Lecture 80 Calculating Partial Derivatives with Autodiff 5:18
Lecture 81 Advanced Partial Derivatives 14:39
Lecture 82 Advanced Partial-Derivative Exercises 6:11
Lecture 83 Partial Derivative Notation 2:27
Lecture 84 The Chain Rule for Partial Derivatives 9:17
Lecture 85 Exercises on the Multivariate Chain Rule 5:18
Lecture 86 Point-by-Point Regression 15:24
Lecture 87 The Gradient of Quadratic Cost 15:16
Lecture 88 Descending the Gradient of Cost 12:53
Lecture 89 The Gradient of Mean Squared Error 23:52
Lecture 90 Backpropagation 5:59
Lecture 91 Higher-Order Partial Derivatives 11:53
Lecture 92 Exercise on Higher-Order Partial Derivatives 2:55

Section 10 : Integral Calculus

Lecture 93 Segment Intro 2:44
Lecture 94 Binary Classification 9:13
Lecture 95 The Confusion Matrix 2:29
Lecture 96 The Receiver-Operating Characteristic (ROC) Curve 9:42
Lecture 97 What Integral Calculus Is 6:14
Lecture 98 The Integral Calculus Rules 5:37
Lecture 99 Indefinite Integral Exercises 2:58
Lecture 100 Definite Integrals
Lecture 101 Numeric Integration with Python 4:51
Lecture 102 Definite Integral Exercise 4:24
Lecture 103 Finding the Area Under the ROC Curve 3:35
Lecture 104 Resources for the Further Study of Calculus 4:1