Knowledge of Statistical analysis and optimization of data

Some key elements that enter much of the discussion on ML:

  1. Basic concepts, expectation values, variance, covariance, correlation functions and errors;
  2. Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
  3. Central elements of Bayesian statistics and modeling;
  4. Central elements from linear algebra
  5. Gradient methods for data optimization
  6. Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;
  7. Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;
  8. Practical optimization using Singular-value decomposition and least squares for parameterizing data.
  9. and much more fun stuff