This repository features a series of interactive Jupyter Notebooks for the Fall term 2021 lab course accompanying the 7,044,1.00 Introduction to Artificial Intelligence and Machine Learning course offered at the University of St. Gallen (HSG).
The goal of this voluntary lab course is to teach some basics in Python programming, machine learning and artificial neural networks. We will also feature information on our coding challenge at the end of the semester here.
| Lab | Date | Topic | Launchers |
|---|---|---|---|
| 0 | < Oct 4 | Testing the Notebook Environment |   |
| 0 | < Oct 4 | Introduction to the Notebook Environment | |
| 1 | Oct 4 | Fundamentals of Python Programming: Data Types and Containers | tba |
| 2 | Oct 18 | Fundamentals of Python Programming: Functions and Decision Structures | tba |
| 3 | Oct 25 | Supervised Learning with Scikit-learn | tba |
| Coding Challenge Quickstart Notebook | tba | ||
| Fall Semester Break! | |||
| Nov 22 | Coding Challenge Kick-off and Team Registration Deadline | ||
| 4 | Nov 22 | Artificial Neural Networks: Training and Evaluation | tba |
| 5 | Dec 6 | Convolutional Neural Network: Training and Evaluation | tba |
| Dec 13 | Coding Challenge Clinic | ||
| Dec 17 | Coding Challenge Final Presentation |
You can run the lab Notebooks in the cloud using either binder or Google Colab, or locally on your computer.
This is the easiest way to run a Notebook in your web browser: just click on the binder badge next to the Notebooks below and off you go. Binder is a service that lets you run Jupyter Notebooks in their cloud at no charge. There is no registration and no login required. However, keep in mind that you cannot save any data or your Notebook file in the cloud (you can save them on your computer, though). Also, starting a binder Notebook can take quite some time, but the performance during runtime is good. For more information, please refer to the Binder documentation.
Similar to binder, you just have to click the Colab badge next to the Notebooks below. All you need is a Google login (e.g., your login information for gmail) and you can use this service at no charge. Two advantages of Colab are that (1) you can save your Notebooks directly into your Google Drive and read data from there, and (2) Google provides you with some limited GPU capabilities free of charge (this will be an interesting feature for the coding challenge.)
If you prefer to run Notebooks locally on your computer, you will need to install Python. If you choose to do so, we recommend to install Anaconda Python, a package that combines the latest version of Python with the most common supplemental modules for data science and machine learning, as well as a Jupyter Notebook server that runs on your computer locally. Anaconda installers are available for the most common operating systems, as well as some detailed installation guides.
To run our Notebooks locally, you can download them individually from this website, or simply clone this repository to your computer.
If you need help running Python and/or Jupyter Notebooks, please don't hesitate to contact us (see below)!
Please don't hesitate to send us your questions to: aiml-teaching ( dot ) ics ( at ) unisg ( dot ) ch