Prerequisite material is overviewed in a series of videos that are available under the "Prerequisites" topic in the navigation bar, which is to the left of this text.

The lectures were produced using technology that is described in this video:
https://youtu.be/ltOxgb28ZKY

No.	PDF	Video
	Week   1
1	Class #01	Introduction To Linear Data Analysis
1a		Course Overview
1b		Course Organization
1c		Matrix Columns
1d		Eigenfacts
2	Class #02	The Graph Adjacency Matrix
2a		Introduction To Graphs
2b		Relevant Definitions For Graphs
2c		The Adjacency Matrix
2d		Non-Bipartite Graphs
3	Class #03	A Graph Laplacian Matrix
3a		The Degree Matrix
3b		A Laplacian Matrix
3c		Properties Of A Laplacian Matrix
3d		The Fiedler Vector
	Week   2
4	Class #04	Vector Spaces
4a		Introduction To Vector Spaces
4b		Block Partitioning A Matrix
4c		Vector-Space Properties
4d		The Column Space
4e		The Null Space
5	Class #05	Spanning Sets And Basis Vectors
5a		Introduction To Vector Spaces
5b		Relevant Definitions For Basis Vectors
5c		Basis Vectors For A Column Space
5d		Orthogonal Subspaces
6	Class #06	Diagonalizable Matrices
6a		Similar Matrices
6b		Diagonalizability
6c		Examples of Diagonalizability
6d		The Matrix Square Root
	Week   3
7	Class #07	Normal Matrices And Spectral Decomposition
7a		Real Normal Matrices
7b		Real Orthogonal Matrices
7c		Real Symmetric Matrices
7d		The Spectral Theorem
8	Class #08	Positive [Semi-]Definite Matrices
8a		Positive Definite Matrices
8b		The Quadratic Form
8c		Mean And Variance Of Data
8d		The Covariance Matrix
9	Class #09	Design Matrix And Standardized Data
9a		Variables And Observations
9b		Variables As Vectors
9c		Zero-Mean Data
9d		Unit-Variance Data
9e		Standardized Data For Regression
9f		Measuring Standard Deviations
	Week   4
10	Test #1	Basic Linear Analysis
11	Class #11	Orthogonal Projection
11a		Concepts In Orthogonal Projection
11b		Projecting A Vector To A Vector
11c		Projecting A Vector To A Vector Space
11d		The Normal Equation
11e		Overdetermined Linear Equations
12	Class #12	Patterns - Linear Regression
12a		Concepts In Statistical Regression
12b		Concepts In Linear Regression
12c		Examples Of Linear Regression
12d		Data Standardization For Linear Regression
12e		Residual Error In Linear Regression
	Week   5
13	Class #13	Cross-Validating Linear Regression
13a		Validation Of Linear Regression
13b		Training Sets And Testing Sets
13c		K-Fold Cross-Validation Of Linear Regression
13d		Examples Of 5-Fold Cross-Validation
14	Class #14	Singular Value Decomposition, Or SVD
14a		Introduction To The SVD
14b		The Left-Transpose Product
14c		The Right-Transpose Product
14d		Structure Of The SVD
15	Class #15	Orthonormal Basis Vectors And The SVD
15a		I Did Not Shoot The SVD
15b		Examples Of The SVD
15c		Matrix Spaces And The SVD
15d		The Null Space And The SVD
15e		Orthonormal Basis Vectors And The SVD
	Week   6
16	Test #2	Matrices and Linear Regression
17	Class #17	Matrix Approximation
17a		Matrix Norms
17b		L2 Matrix Norm And Frobenius Matrix Norm
17c		Matrix Series From The SVD
17d		Low-Rank Approximations
17e		Scree Plot Of Singular Values
18	Class #18	Principal Components Analysis, Or PCA
18a		Introduction To PCA
18b		PCA From Covariance Matrix
18c		PCA As Spectral Decomposition
18d		Computing Data Scores Using PCA
	Reading Week
		Holiday Special
	Week   7
19	Class #19	Unsupervised Learning - K-Means Clustering
19a		A Conceptual Hierarchy Of Machine Learning
19b		Hyperplane of Separation
19c		Basics Of Vector Clustering
19d		A K-Means Clustering Algorithm
19e		Clustering The Iris Data
20	Class #20	Classification - Linear Separability
20a		Separating Two Clusters
20b		A Hyperplane From Cluster Centroids
20c		Hyperplanes For Multiple Clusters
20d		The Davies-Bouldin Index For Clusters
21	Class #21	PCA - Matrix Algebra and Dimensionality Reduction
21a		Revisiting PCA
21b		The Scatter Matrix Of Variables For PCA
21c		PCA As Matrix Approximation
21d		PCA As Dimensionality Reduction
	Week   8
22	Test #3	The SVD, PCA, And Dimensionality Reduction
23	Class #23	PCA And The Rayleigh Quotient
23a		PCA, Scores, And The SVD
23b		PCA Maximizes A Linear Transformation
23c		The Rayleigh Quotient And PCA
24	Class #24	Patterns - Linear Discriminant Analysis, or LDA
24a		Finding Patterns In Labeled Data
24b		Example Of PCA For Labeled Data
24c		Fisher's Linear Discriminant, Or LDA
24d		Example Of LDA For Labeled Data
24e		Example Of LDA For Iris Data
	Week   9
25	Class #25	Classification - Assessment With Confusion Matrix
25a		Data Labels As Dependent Variables
25b		Confusion Matrix For Binary Labels
25c		Example Of Confusion Matrix
26	Class #26	Classification - Assessment With ROC Curve
26a		Receiver Operator Characteristic, Or ROC
26b		ROC And The Confusion Matrix
26c		Example ROC Curve For Fictitious Virus
27	Class #27	Classification - Single Artificial Neuron
27a		Artificial Neurons
27b		Data Flow And Computations For Neurons
27c		Hyperplane Separation For Neurons
	Week   10
28	Test #4	LDA, Assessment, Odds Of Occurrence
29	Class #29	Odds Of Occurrence And Probability
29a		Odds Of Hyperplane Classification
29b		Odds And Probability
29c		The Logistic Function For Odds
29d		Properties Of The Logistic Function
30	Class #30	Supervised Learning - Perceptron Rule
30a		Perceptron Model Of Neurons
30b		Deriving The Perceptron Rule
30c		Pseudocode For The Perceptron Rule
30d		Perceptron Rule For Iris Data
	Week   11
31	Class #31	Classification - Logistic Regression
31a		Shortcomings Of Perceptrons
31b		Scores From Logistic Activation
31c		Residual Error Of Scores
31d		Logistic Regression For Iris Data
32	Class #32	Nonlinear Separation - Embeddings And Gram Matrix
32a		Some Data Are Nonlinearly Separable
32b		Embedding A Vector Space
32c		Gram Matrix For An Embedding
33	Class #33	Nonlinear Separation - Kernel PCA
33a		High-Dimensional PCA
33b		Scatter Matrix Of Observations
33c		Kernel PCA Using The Gram Matrix
33d		Kernel PCA For Iris Data
	Week   12
34	Test #5	Machine Learning
35	Class #35	Spectral Clustering Of Data
35a		Fielder Vector And Spectral Decomposition
35b		Spectral Clustering Using Eigenvectors
35c		Distance Matrix And Spectral Clustering
36	Class #36	The Curse Of Dimensionality
36a		The Curse of Dimensionality
36b		Dimensionality And Hypercube Vertices
36c		Dimensionality And Uniform Distributions
36d		Dimensionality And Gaussian Distributions
37	Class #37	Course Summary
37a		Course Summary
37b		Unplanned Recording Events
	Extra Material
	Table of Contents
	References

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