The course image for I2ML Part 2
AI Campus Original
Introduction to Machine Learning Part 2: Algorithms
4 weeks à 10 hours
Record of Achievement
For free
This course belongs to the course series Introduction to Machine Learning


Machine Learning (ML) is at the core of many applications of artificial intelligence. A key goal of this course series is to teach the fundamental building blocks behind supervised ML. In this second part, we will present to you a variety of machine learning algorithms such as k-nearest neighbors, classification and regression trees, random forests, and neural networks.

Which topics will be covered?

  • Theoretical understanding of different ML algorithms such as k-nearest neighbors, Classification and Regression Trees, Random Forests, and Neural Networks

  • Advantages and disadvantages of the different learners

  • Application of the learned algorithms in R and Python

What will I achieve?

  • Explain the idea of k-NN

  • Explain the idea of classification and regression trees and how random forests improve this method

  • Explain how a neural network works

  • Apply the learned ML algorithms to real-world data using R and Python

Which prerequisites do I need to fulfill?

This course is open to all who are interested. However, we recommend learners to have:

  • A strong foundation in mathematics, such as 8 years of math education in secondary schools

  • Pre-knowledge in linear algebra and analysis required (at least high school level)

  • Pre-knowledge in statistics and probability is recommended (at least high school level)

  • Basic programming skills in R or Python (e.g., through a small self-study course)

  • You have concluded the course Introduction to Machine Learning Part 1

The course image for I2ML Part 2
This course is offered by

Dr. Ludwig Bothmann

Ludwig-Maximilians-Universität München - Institut für Statistik
Munich Center for Machine Learning
Course information
Learning format:
Online course
The creators of the learning opportunities are responsible for their content.
Machine Learning
Data Science and Big Data