Knowledge of Statistical analysis and optimization of data
Some key elements that enter much of the discussion on ML:
- Basic concepts, expectation values, variance, covariance, correlation functions and errors;
- Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
- Central elements of Bayesian statistics and modeling;
- Central elements from linear algebra
- Gradient methods for data optimization
- Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;
- Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;
- Practical optimization using Singular-value decomposition and least squares for parameterizing data.
- and much more fun stuff