Content, progress and pedagogy of the module

Purpose:
After attending the course the students have developed the engineering intuition of the fundamental concepts and results of probability and statistics. They are able to apply the taught material to model and solve simple engineering problems involving randomness.

Learning objectives

Knowledge

• Must have knowledge about the concept of probability spaces
• Must have knowledge about the conceptual models of estimation and hypothesis testing
• Must be able to understand the basic concepts of probability theory, i.e., probability of events, random variables, etc.
• Must be able to understand basic concepts of statistics such as binary hypothesis testing

Skills

• Must be able to apply/compute Bayes rule in simple contexts
• Must be able to determine the probability that Binomial, Poisson, and Gaussian random variables take values in a specified interval
• Must be able to determine the mean and variance of Binomial, Poisson, and Gaussian random variables
• Must be able to determine the marginal distributions of multi-variate Gaussian variables
• Must be able to apply and interpret ML-estimation in simple contexts involving the Binomial, Poisson, and Gaussian distribution
• Must be able to apply and interpret binary-hypothesis tests in simple contexts involving the Binomial, Poisson, and Gaussian distribution

Competences

• Must be able to apply the general concepts of probability theory and statistics in a new, simple context. This includes choosing suitable methods, evaluating outcomes, and making the appropriate conclusions

Type of instruction

See the general description of the types of instruction described in the introduction to Chapter 3.

Exam

Exams