**Data. **Summarizing data. Visualizing
data. The fundamental problem of data analysis: uncertainty in our
understanding of the data generating process.

**Probability.** The axiomatic, Bayesian
and classical (frequentist) views of probability. Joint, marginal and
conditional densities. Bayes theorem.

**Distributions.** Common probability
distributions. Distributions as a means for expressing probabilistic opinions.
Distributions as data generators.

**Fundamental statistical techniques.**
Monte Carlo integration. Bootstrap. Maximum likelihood estimation. Bayesian
inference.

**Basic statistical tasks.** Hypothesis
testing vs Bayesian estimation.

**The multivariate normal distribution.**As a linear transformation. Linear regression. PCA.

- nosilec: Erik Štrumbelj
- nosilec: Aljaž Zalar