This course offers an introductory and applied overview of “supervised” Machine Learning. This includes:
- ML Basics
- Supervised Regression
- Supervised Classification
- Performance Evaluation
- Classification and Regression Trees
- Random Forests
- Parameter Tuning
- Practical Advice
The course is of an introductory nature and geared towards students with some statistics background. It is aimed at a practical and operational understanding of the covered algorithms and models, with less emphasis on theory and formalism. The accompanying exercises, demos and tutorials are a mix of theoretical and p ractical assignments, the latter in
R (mostly with
The course is organized as a digital lecture, which should be as self-contained and enable self-study as much as possible. The major part of the material is provided as slide sets with lecture videos. We have also prepared interactive tutorials where you can answer multiple choice questions, and learn how to apply the covered methods in
R on some short coding exercises. Our plan is to extend this self-study material over the next months and years.
The course is targeted at ML beginners with a basic, university level, education in maths and statistics:
- Basic linear algebra: vectors, matrices, determinants
- Simple calculus: derivatives, integrals, gradients
- Some probability theory: probability, random variables, distributions
- Basic statistics knowledge: descriptive statistics, estimators.
(Linear) modelling from a statistics perspective will help, but is not required.
- Working knowledge of
Data Science in a Box follows a similar philosophy as this course, with much more emphasis on learning R, doing data visualization and understanding more classical statistical inference methods (hypothesis tests, linear regression, etc.)