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00:00:00 - Introduction
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Classroom Contents
Linear Algebra for Machine Learning
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- 1 00:00:00 - Introduction
- 2 00:02:09 - Essential Trigonometry and Geometry Concepts
- 3 00:10:52 - Real Numbers and Vector Spaces
- 4 00:15:05 - Norms, Refreshment from Trigonometry
- 5 00:19:52 - The Cartesian Coordinates System
- 6 00:24:37 - Angles and Their Measurement
- 7 00:38:00 - Norm of a Vector
- 8 00:44:08 - The Pythagorean Theorem
- 9 00:52:00 - Norm of a Vector
- 10 00:56:00 - Euclidean Distance Between Two Points
- 11 01:11:33 - Foundations of Vectors
- 12 01:12:50 - Scalars and Vectors, Definitions
- 13 01:42:28 - Zero Vectors and Unit Vectors
- 14 01:49:39 - Sparsity in Vectors
- 15 01:52:39 - Vectors in High Dimensions
- 16 01:55:14 - Applications of Vectors, Word Count Vectors
- 17 02:03:22 - Applications of Vectors, Representing Customer Purchases
- 18 02:39:22 - Advanced Vectors Concepts and Operations
- 19 02:40:40 - Scalar Multiplication Definition and Examples
- 20 03:04:27 - Linear Combinations and Unit Vectors
- 21 03:51:37 - Span of Vectors
- 22 04:31:42 - Linear Independence
- 23 05:03:34 - Linear Systems and Matrices, Coefficient Labeling
- 24 05:20:24 - Matrices, Definitions, Notations
- 25 05:50:24 - Special Types of Matrices, Zero Matrix
- 26 06:25:25 - Algebraic Laws for Matrices
- 27 07:21:56 - Determinant Definition and Operations
- 28 08:12:47 - Vector Spaces, Projections
- 29 08:20:05 - Vector Spaces Example, Practical Application
- 30 09:14:33 - Vector Projection Example
- 31 09:29:35 - Understanding Orthogonality and Normalization
- 32 10:06:29 - Special Matrices and Their Properties
- 33 10:21:07 - Orthogonal Matrix Examples
