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)