1630 Introduction to Machine Learning

FT, BSc Computer Science
6 ECTS, Lecture + Exercises

Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. It allows computers to learn and make decisions without being explicitly programmed, by identifying patterns in data and improving its ability to perform specific tasks. In this course we will understand how ML can be applied to solve real-world problems and gain a solid foundation in classical ML.

This course provides an introduction to concepts, methods, best practices and theoretical foundations of standard machine learning algorithms. Topics covered include:

  • ML basics
  • Regression vs. classification
  • Supervised learning: The goal is to learn functional dependencies for classification and regression. We cover linear systems, basis function approaches, and kernel approaches.
  • Unsupervised learning: This is about describing important structures in the data in a compact way. Typical examples are clustering and Principal Component Analysis (PCA), which allow a unified description of high-dimensional probabilistic dependencies.

The technical topics are illustrated with a number of real-world applications.