190.013 Introduction to Machine Learning Lab (2SH P, SS)


This exercise is part of the lecture 190.012 Introduction to Machine Learning

Enrolling for this exercise is a highly recommended but not a requirement for passing the machine learning lecture. 

Course Resources

Python Videolectures and Tutorials

Location & Time


Course Topics

The exercise is based on multiple short (typically 2-4 pages) assignments. For most assignments a written report in Latex and Python Code has to be submitted via Email. Each student has to submit an individual assignment report and code.

The topics of the assignments are

  • Creating Latex Documents & Data handling (Latex env. setup, report template, reading & editing data files),
  • Programming in Python & Probability Theory (Python basics, Editor PyCharm, Workflow, variables, functions, classes, plotting, Gaussian distributions, sampling, plotting),
  • Linear Probabilistic Regression (features, least-squares regression derivation & implementation, regularization),
  • Nonlinear Probabilistic Regression (Gaussian Processes, implementation, kernels, predictions)
  • Probabilistic Time-Series-Models (Implementation & Learning from real-world data, Visualization, Predictions).
Details to the grading will be presented in the first exercise on the 22.02.2022. For each assignment code templates will be provided.

Learning objectives / qualifications

Hands-on experience with machine learning methods.