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



  • Assignment 1: Preliminaries and Introduction to Google Collab and Python 
  • Assignment 2: Programming in Python, Probability Theory & Linear Algebra operations
    • python classes
    • gaussian distributions
    • sampling
    • plotting
    • Linear Algebra
  • Assignment 3: Probability Theory
    • Classes for Distributions
    • Plotting & Sampling
    • Bayes Theorem
  • Assignment 4Linear Regression with Diabetes Dataset 
    • Data Preprocessing
    • Least Squares Regression
    • Ridge Regression
    • Lasso Regression
  • Assignment 5: Non-Linear Regression & Classification 
    • Perceptron
    • Non-linear Feature Transformations (Polynomial and RBFs)
    • Neural Network
  • Assignment 6: Probabilistic Time Series Models ( Gaussian Process) 

 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


Fotios Lygerakis via email at fotios.lygerakis@unileoben.ac.at