Content, progress and pedagogy of the
The goal of the course is to enable students to develop the
engineering intuition of the fundamental concepts and results of
probability, statistics, and stochastic processes, and to apply the
taught material to model and solve problems relevant to engineers
- Must have knowledge about fundamental concepts in probability,
including conditional probability and independence.
- Must have knowledge about discrete and continuous random
variables and relevant properties of these.
- Must have knowledge about various examples of descriptive
statistics, e.g. histograms and scatterplots.
- Must have knowledge about statistical inference, including
estimation, confidence intervals and hypothesis testing.
- Must have knowledge about important statistical models, like
linear regression (simple and multiple), analysis of variance,
logistic regression and log-linear models (in particular
- Must be able to, given specific data, specify a relevant
statistical model and account for the assumptions and limitations
of the chosen model.
- Must be able to use relevant software for carrying out the
statistical analysis of given data and be able to interpret the
results of the analysis.
- Must be able to apply/compute:
- Bayes rule in simple contexts
- The probability that Binomial, Poisson, and Gaussian random
variables take values in a specified interval
- The mean and variance of Binomial, Poisson, and Gaussian random
- The marginal distributions of multi-variate Gaussian
- Must be able to apply and interpret:
- ML-estimation in simple contexts involving the Binomial,
Poisson, and Gaussian distribution
- Binary-hypothesis tests in simple contexts involving the
Binomial, Poisson, and Gaussian distribution
- Must be able to judge the applicability of statistics within
- Must be capable of performing a critical judgement of the
results of a statistical analysis.
- Must be capable of communicating the results of a statistical
analysis to people with no or little background within
Type of instruction
Lectures in combination with practical exercises and self-study
and mini-projects (using, e.g. MATLAB).