This repository hosts the initial exercises for the subject Machine Learning I in the Master in Artificial Intelligence, which is jointly offered by the three Galician universities: the University of A Coruña (UDC), the University of Santiago de Compostela (USC), and the University of Vigo (UVigo).
The notebooks in this repository are based on the initial work of Daniel Rivero Cebrián, a former instructor of the subject, who generously provided materials to support the current development.
The practical sessions will be conducted using Julia, a widely used language in machine learning research. These exercises have been tested on Julia version 1.7.2, though they should also be compatible with versions starting from 1.2.0.
- Enrique Fernández Blanco (Course Coordinator, UDC)
- Víctor M. Darriba Bilbao (UVigo)
- Nelly Condori Fernández (USC)
A Docker image has been prepared with all necessary libraries and configurations. It’s based on the Jupyter Docker image and includes:
- Jupyter Lab = 4.0.5
- Julia = 1.9.3
| Julia Library | Version |
|---|---|
| CSV | 0.10.11 |
| DataFrames | 1.6.1 |
| DelimitedFiles | 1.9.1 |
| FileIO | 1.16.1 |
| Flux | 0.14.5 |
| IJulia | 1.24.2 |
| Images | 0.26.0 |
| JLD2 | 0.4.33 |
| MAT | 0.10.5 |
| Plots | 1.39.0 |
| Pluto | 0.19.27 |
| ScikitLearn | 0.7.0 |
| StatsPlots | 0.15.6 |
| Tables | 1.10.1 |
| XLSX | 0.10.0 |
| Statistics | 1.9.0 |
- Python = 3.11.2
| Python Library | Version |
|---|---|
| IPyKernel | 6.25.1 |
| jupyter-pluto-proxy | 0.1.2 |
| Matplotlib | 3.7.2 |
| Numpy | 1.25.2 |
| Pandas | 2.1.0 |
| Plotly | 5.16.1 |
| rich | 13.5.2 |
| seaborn | 0.12.2 |
There are two ways to set up the Docker environment:
If you have cloned the repository, build the Docker image using the following command:
docker built -t ennanco/machinelearning1 docker/
This build process takes approximately 15 minutes.
Alternatively, you can download the pre-built image from Docker Hub:
docker pull ennanco/machinelearning1
The download size is around 2 GB, so the time required will depend on your internet speed.
To start the Docker environment, navigate to the cloned folder and run:
docker run -p 8888:8888 -v ${PWD}/.:/home/jovyan/work ennanco/machinelearning1
This command will open a Jupyter Lab environment in your browser, pre-configured with all necessary libraries for the subject.