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 Methods for AI
1a		Course Overview
1b		Course Organization
1c		Matrix Columns
1d		Eigenfacts
2	Class #02	Graphs: Adjacency Matrix and Laplacian Matrix
2a		Introduction To Graphs
2b		Relevant Definitions For Graphs
2c		The Adjacency Matrix
2d		Non-Bipartite Graphs
2e		The Degree Matrix
2f		A Laplacian Matrix
2g		Properties Of A Laplacian Matrix
2h		The Fiedler Vector
3	Class #03	Vector Spaces
3a		Introduction To Vector Spaces
3b		Block Partitioning A Matrix
3c		Vector-Space Properties
3d		The Column Space
3e		The Null Space
	Week   2
4	Class #04	Spanning Sets And Basis Vectors
4a		Introduction To Vector Spaces
4b		Relevant Definitions For Basis Vectors
4c		Basis Vectors For A Column Space
4d		Orthogonal Subspaces
5	Class #05	Diagonalizable Matrices
5a		Similar Matrices
5b		Diagonalizability
5c		Examples of Diagonalizability
5d		The Matrix Square Root
6	Class #06	Spectral Decomposition and Positive [Semi-]Definite Matrices
6a		Real Normal Matrices
6b		Real Orthogonal Matrices
6c		Real Symmetric Matrices
6d		The Spectral Theorem
6e		Positive Definite Matrices
6f		The Quadratic Form
6g		Mean And Variance Of Data
6h		The Covariance Matrix
	Week   3
7	Class #07	Design Matrix And Standardized Data
7a		Variables And Observations
7b		Variables As Vectors
7c		Zero-Mean Data
7d		Unit-Variance Data
7e		Standardized Data For Regression
7f		Measuring Standard Deviations
8	Class #08	Orthogonal Projection
8a		Concepts In Orthogonal Projection
8b		Projecting A Vector To A Vector
8c		Projecting A Vector To A Vector Space
8d		The Normal Equation
8e		Overdetermined Linear Equations
9	Quiz #1	Matrix Properties and Vector Spaces
	Week   4
10	Class #10	Patterns - Linear Regression
10a		Concepts In Statistical Regression
10b		Concepts In Linear Regression
10c		Examples Of Linear Regression
10d		Data Standardization For Linear Regression
10e		Residual Error In Linear Regression
11	Class #11	Cross-Validating Linear Regression
11a		Validation Of Linear Regression
11b		Training Sets And Testing Sets
11c		K-Fold Cross-Validation Of Linear Regression
11d		Examples Of 5-Fold Cross-Validation
12	Test #1	Matrix Properties and Vector Spaces
	Week   5
13	Class #13	Singular Value Decomposition, Or SVD
13a		Introduction To The SVD
13b		The Left-Transpose Product
13c		The Right-Transpose Product
13d		Structure Of The SVD
14	Class #14	Orthonormal Basis Vectors And The SVD
14a		I Did Not Shoot The SVD
14b		Examples Of The SVD
14c		Matrix Spaces And The SVD
14d		The Null Space And The SVD
14e		Orthonormal Basis Vectors And The SVD
15	Quiz #2	Matrices and Linear Regression
	Week   6
16	Class #16	Principal Components Analysis, Or PCA
16a		Introduction To PCA
16b		PCA From Covariance Matrix
16c		PCA As Spectral Decomposition
16d		Computing Data Scores Using PCA
16e		Matrix Norms
17f		L2 Matrix Norm And Frobenius Matrix Norm
16g		Matrix Series From The SVD
17	Class #17	PCA - Matrix Algebra and Dimensionality Reduction
17a		Revisiting PCA
17b		The Scatter Matrix Of Variables For PCA
17c		PCA As Matrix Approximation
17d		PCA As Dimensionality Reduction
17e		Low-Rank Approximations
17f		Scree Plot Of Singular Values
18	Test #2	Matrices and Linear Regression
	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	Quiz #3	The SVD, PCA, And Dimensionality Reduction
	Week   8
22	Class #22	Classification - Assessment With Confusion Matrix
22a		Data Labels As Dependent Variables
22b		Confusion Matrix For Binary Labels
22c		Example Of Confusion Matrix
23	Class #23	Classification - Assessment With ROC Curve
23a		Receiver Operator Characteristic, Or ROC
23b		ROC And The Confusion Matrix
23c		Example ROC Curve For Fictitious Virus
24	Test #3	The SVD, PCA, And Dimensionality Reduction
	Week   9
25	Class #25	Odds Of Occurrence And Probability
25a		Odds Of Hyperplane Classification
25b		Odds And Probability
25c		The Logistic Function For Odds
25d		Properties Of The Logistic Function
26	Class #26	Classification - Single Artificial Neuron
26a		Artificial Neurons
26b		Data Flow And Computations For Neurons
26c		Hyperplane Separation For Neurons
27	Quiz #4	Classification Assessment, Odds Of Occurrence
	Week   10
28	Class #28	Classification - Logistic Regression
28a		Shortcomings Of Perceptrons
28b		Scores From Logistic Activation
28c		Residual Error Of Scores
28d		Logistic Regression For Iris Data
29	Class #29	Nonlinear Separation - Embeddings And Gram Matrix
29a		Some Data Are Nonlinearly Separable
29b		Embedding A Vector Space
29c		Gram Matrix For An Embedding
30	Test #4	Classification Assessment, Odds Of Occurrence
	Week   11
31	Class #31	Nonlinear Separation - Kernel PCA
31a		High-Dimensional PCA
31b		Scatter Matrix Of Observations
31c		Kernel PCA Using The Gram Matrix
31d		Kernel PCA For Iris Data
32	Class #32	Spectral Clustering Of Data
32a		Fielder Vector And Spectral Decomposition
32b		Spectral Clustering Using Eigenvectors
32c		Distance Matrix And Spectral Clustering
33	Quiz #5	Machine Learning
	Week   12
34	Class #34	The Curse Of Dimensionality
34a		The Curse of Dimensionality
34b		Dimensionality And Hypercube Vertices
34c		Dimensionality And Uniform Distributions
36d		Dimensionality And Gaussian Distributions
35	Class #35	Course Summary
35a		Course Summary
36	Test #5	Machine Learning
37b		Unplanned Recording Events
	Extra Material
	Table of Contents
	References

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