This course will give a basic introduction to using pre-trained machine learning models in Python.

The first part of the course, participants will become familiar with the basic elements of the Python programming language. What is a variable? What is a function? What is a datatype? What is an abstract datatype? Then we will also look into a few use cases like reading/writing data from files, loading images, and accessing data sources/API’s on the Web. Furthermore, we will look at the basic functionality of the PyTorch machine learning library and how to implement simple linear regression and logistic regression with it.

We will mainly use Google Colab notebooks, but we will also briefly look at how Python can be installed and used on your own computer.

In the second part, participants will gain practical skills in the use of existing machine learning models, as well as training some of your own. You will get to explore two machine learning libraries, PyTorch and Tensorflow, all within the cloud-based python development environment, Colab. The focus will be on framing a problem, devising a data generation pipeline, and finally training, running, and evaluating your own machine learning models. Participants will be required to present their results.

No unexcused absence from classes. Active participation in class (20%), delivery of programming assignments (40%), presentation of final results (40%).

This is the first part of the course (Python introduction), please register for the second part (https://base.uni-ak.ac.at/courses/2021S/S03716/) as well!