Review basic math

• Linear algebra
• Calculus
• Optimization
• Probability
  • including statistics and information theory

Linear algebra

Matrix/vector addition and multiplication

Eigen value/vector

Calculus

Differentiation

• single- and multi-variable

Vector calculus

• gradient

Optimization

Critical points

• local/global $\times$ minimum/maximum/saddle

Gradient descent

Constrained optimization

• Lagrangian multiplier

Probability

Probability space

• discrete - probability mass function
• continuous - probability density function

Statistics

• expectation
• bias and variance

Conditional probability

• will need this for Bayesian estimation

Information theory

• entropy (example using A, C, G, T letters for genome)