Lab tutorials
Each lab tutorial is presented as a Google Colab Notebook. This will allow you to run snippets of code interactively on a web interface.
To be able to save any changes you make to the notebook, please save a copy of the notebook to your own Google Drive, and run your own copy of the notebook on Google Colab. This is the easiest and recommended way to work on these tutorials.
Alternatively, you can download the notebook as an *.ipynb file and run it locally on your machine with Jupyter Notebook. A quick tutorial on Jupyter Notebook is available here.
If you have the notebook somewhere in your home directory on the departmental servers, and wish to run Jupyter Notebook/Lab remotely, search for “To use Jupyter Lab” on this page. Alternatively, CSG is testing a JupyterHub service that saves you the hassle of doing port forwarding. See the tutorial on the next page for more details if you are interested.
Lab tutorials
- Lab 1: Building a Machine Learning Pipeline
- Lab 2: K-Nearest Neighbours
- Lab 3: Machine Learning Evaluation
- Lab 4: Simple Linear Regression
- Lab 5: Multiple Linear & Logistic Regression, and PyTorch
- Lab 6: Unsupervised Learning
- Lab 7: Evolutionary Algorithms