# 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

## Lectures

**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 4:**Linear 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 Programming Tutorial by Socratica [recommended]
- MUOnline course link
- Latex Template for the Assignments
- Introduction to LaTex
- Introduction to Python
- Multi-Threading Jupiter Tutorial by Vedant Dave

## Python Videolectures and Tutorials

## Contact

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