Basic concepts such as probability, conditional probability and independent events. Discrete and continuous stochastic variables, especially one-dimensional stochastic variables. Location, spread and dependency measures for stochastic variables and data sets. Common distributions and their model situations, including the normal distribution, the binomial distribution and the Poisson distribution. The Central limit value theorem and the Law of large numbers.
Descriptive statistics. Point estimates and general estimation methods such as the Maximum likelihood method and the Minimum square method. General confidence intervals but special confidence intervals for expected value and variance in normal distribution. Confidence interval for participations and difference in expected values and participations. Hypothesis testing. Chi2 test of distribution, homogeneity test and independence test. Linear regression.
Machine learning paradigms, appoaches and applications. Supervised / unsupervised learning, generalization, model selection, validation and evaluation, probabilistic methods, dimensionality reduction and representations.