Machine Learning (880083-M-6)

Complete guide through all notebooks. Each type of exercise clearly explained.

Detailed cheatsheet with all important notes for the final exam of the course Machine Learning. I passed the exam by only studying this cheatsheet.

As per request; a full cheatsheet with a single page of code and a page with notes. As the theoretical questions stayed relatively general, this should suffice. Practice the coding, understand what Gregorz asks you to do and ace the exam! Also, don't forget to bring your glasses. For me, it was perfectly readable, but I reckon for those with glasses, it might be slighly harder. I apologize. There is some minimal room left on the second page to write. I aso included some details from another mac...

Very useful summary for the Machine Learning course at Tilburg University (Data Science and Society). Summary contains all parts that are discussed in the course (2019) extra examples, explanation and notes. Summary also contains many useful visualizations.

CONTENT VERY SIMILAR TO 2019/2020 EXAM. Heard from many students that they passed the exam with the help from this document! Thanks for the feedback. Also have a look at my cheatsheet including code! A complete overview of all lectures, including slide snippets and additional notes for the course on Machine Learning by Dr. Gregorz. Hopefully, this should be enough to support you to understand the basics of Machine Learning as this document summarizes all lecture slides and everything he told....

Voor het tentamen Machine Learning is het toegestaan om een cheatsheet van 1A4 bij te houden. Deze is speciaal gemaakt zodat alle onderwerpen van dit vak uitgebreid zijn beschreven, zoals Decision Trees, Perceptron, Gradient Descent, Feature Engineering, Logistic Regression en Neural Networks. English: for the Machine Learning exam, you are allowed to bring one cheatsheet (1A4) with you. On this cheatsheet, all the necessary information (and more) are on just two sides of paper. The following ...

Full summary including an introduction of Machine Learning and algorithms, such as Decision Tree, Perceptron, Gradient Descent, Logistic Regression (classifier) and Neural Networks. This summary also includes a section about Feature Engineering. Extra context and illustrations/graphs are also given, which makes this field of study a bit more understandable.