AI class prerequisites: Difference between revisions
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== Probability (Part 7) - Bayes' Rule == | == Probability (Part 7) - Bayes' Rule == | ||
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== Probability (Part 8) - More Bayes' Rule == | == Probability (Part 8) - More Bayes' Rule == | ||
== Introduction to Random Variables == | == Introduction to Random Variables == |
Revision as of 09:45, 22 October 2011
Here we document the prerequisite learning for the AI class.
Resources
- Probability Prerequisites
- Linear Algebra Prerequisites
- Introduction to Matrices
- Matrix Multiplication (Part 1)
- Matrix Multiplication (Part 2)
- Inverse Matrix (Part 1)
- Inverting Matrices (Part 2)
- Inverting Matrices (Part 3)
- Matrices to Solve a System of Equations
- Singular Matrices
- Introduction to Vectors
- Vector Dot Product and Vector Length
- Defining the Angle Between Vectors
- Cross Product Introduction
- Matrix Vector Products
- Linear Transformations as Matrix Vector Products
- Linear Transformation Examples: Scaling and Reflections
- Linear Transformation Examples: Rotations in R2
- Introduction to Projections
- Exploring the Solution Set of Ax = b
- Transpose of a Matrix
- 3x3 Determinant
- Introduction to Eigenvalues and Eigenvectors
Probability Prerequisites
Basic Probability
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Probability (Part 6) - Conditional Probability
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Probability (Part 7) - Bayes' Rule
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