We will cover some basics of statistics and probabilities for machine learning and data science, such as:

- How to treat new data,
- How to get a descriptive overview of it
- Probabilistic intuitions and the Bayes theorem
- Underfitting, overfitting and the bias variance trade-off
- Real-world case studies

Machine learning systems and real-world datasets can fool us without a grounded understanding of these basic concepts, and so we will give particular attention to describe and identify the ways this can be avoided.

There is no prerequisite beyond high school mathematics.