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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 (Twitter URL)
  • Xavier Hernandez Alias
  • Miquel Anglada Girotto
  • Anamaria Elek
  • Sonal Rashmi