Prerequisites

Maths & Statistics

The course is targeted at ML beginners with a basic, university-level, education in maths and statistics and programming. The first part aims at a practical and operational understanding of concepts. It requires a basic (!) understanding of:

• Linear algebra: vectors, matrices
• Multivariate calculus: derivatives, gradients, integrals
• Probability theory: random variables, distributions, expectation and variance
• Statistics: not a lot – parameter estimation and linear modeling from a statistics perspective will help but is not required

The second part focuses on theoretical foundations and more complex algorithms. The general prerequisites are not really that different, but we often go more in-depth into topics, and some parts also contain proofs.

Programming Language

All of the covered concepts and algorithms are presented independently of any programming language. But of course we also offer a large variety of applied exercises and notebooks. These are currently in R, and often use the mlr3 package. We are working on offering the exercises in Python as well.