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

This course accompanies the 190.012 Introduction to Machine Learning lecture.

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

Format, Location & Time

  • Format: The course format is physical attendance. The lectures are going to be broadcasted via Webex too, but the focus will be given only to participants physically present.
  • Location: HS Thermoprozesstechnik
  • Broadcast: WEBEX
  • Dates: Fridays 13:15-15:00



  • Lecture 1: Preliminaries and Introduction to Google Collab and Python [grades]
  • Lecture 2: Programming in Python, Probability Theory & Linear Algebra operations
    • python classes
    • gaussian distributions
    • sampling
    • plotting
    • Linear Algebra
  • Assignment 3: Linear Probabilistic Regression
    • features
    • least-squares regression derivation & implementation
    • regularization
  • Assignment 4: Nonlinear Probabilistic Regression
    • gaussian processes implementation
    • kernels
    • predictions
  • Assignment 5: 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 03.03.2023. For each assignment code templates will be provided.

Learning objectives / qualifications

Hands-on experience with machine learning methods.

Course Resources​

Python Videolectures and Tutorials