Introduction to Machine Learning

Welcome to our first course on introducting the basic concepts underlying machine learning and how they can be applied. The course is split in 4 sessions:

1. What is Machine Learning?
2. Supervised Learning
3. Unsupervised Learning
4. Dimensionality Reduction

Every session is based on a jupyter notebook that you can execute via https://colab.research.google.com.

Requirements

• sklearn
• pandas
• numpy
• matplotlib
• seaborn

Run it locally

Navigate on the terminal into this folder and run it with either:

jupyter notebook/lab:

jupyter notebook or jupyter lab

docker:

docker build -t ml_cnag-crg_notebook .

#notebook
docker run -p 8888:8888 ml_cnag-crg_notebook 

#lab
docker run -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes ml_cnag-crg_notebook

docker-compose:

#notebook
docker-compose up

#lab
#uncomment below in docker-compose.yaml to use jupyterlab
    #environment: 
        #- JUPYTER_ENABLE_LAB=yes

Contributors

CRG-CNAG PhD students:

• Savvas Kourtis
• Thnee Mackensen
• Sergi Beneyto Calabuig
• Ivo Christopher Leist ()
• Xavier Hernandez Alias
• Miquel Anglada Girotto
• Anamaria Elek
• Sonal Rashmi