Course Outline

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There are a lot of pieces of knowledge that we need to fit together.

Last updated: 2018-09-14

Statistics

• Expected value (mean), variance, covariance. Algebraic properties.
• Standard deviation.
• Random variables. Distributions (probability density function).
• Standard normal distribution (“Gaussian”) with $\mu = 0$ and $\sigma = 1$.
• Scaling to get mean = 0, variance = 1.
• Area under normal distribution.
• Computations.

Linear algebra

• Shapes. Transpose. Inverse. Determinant?
• Matrix-vector product.
• Dot product. Projection onto a vector.
• Solving linear systems.
• Derivative of matrix function like (X.w)^2 ?

Regression

• Not sure.
• Do it and see that the method works.