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Science des Données Saison 5: Technologies pour l'apprentissage automatique / statistique de données massives et l'Intelligence Artificielle

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Artificial Intelligence Frameworks

This course follows the Machine Learning and the High Dimensional & Deep Learning courses. In theses courses, you have acquired knowledge in machine and deep learning algoritms and their application on various type of data. This knowledge is primordial to become a DataScientist.

This course has three main objectives. You will

  1. learn how to apply efficiently these algorithms using

  2. discover new field of artificial intelligence applied on (real) datasets that require specific algorithms:

    • Text.
    • Video Game
      • Algorithms: Reinforcement learning, (Policy Gradient algorithm, Q-Learning, Deep Q-learning)
      • *Libraries : AI Gym, Tensorflow.
    • Movies Notations
      • Algorithms: Recommendation system, (User/User and Item/Item filters, NMF, Neural recomendation system)
      • *Libraries : Surprise, Tensorflow.
  3. how to efficiently share reproducible code.

    • Build a Github repository.

NB: Some contents from previous years are still available on the repository (like Spark) but are not covered during theses courses anymore.

Knowledge requirements

Schedule

  • Lectures : 10 hours
  • Practical Works : 30 hours.

The course is divided in 5 topics (of various lentgh) over 5 days.

Course introduction + Github Reminder: Slides/Video

  • Session 1 - 02-11-20

  • Session 2 - 16-11-20

    • Text: Recurrent Network
    • Development for Data Scientist: Python environment + Github Repo + Python Script.
  • Session 3 - 30-11-20

    • Development for Data Scientist: Introduction to Google Cloud Computing.
    • Development for Data Scientist: Docker
  • Session 4 - 07-12-20

    • Introduction to deep Reinforcement learning: Deep Q-learning
      • Slides / Video
      • Q Learning reminder: TP
      • Deep Q Learning on cartpole: TP
      • Deep Q Learning on Gridworld: TP
  • Session 5 14-12-20

  • Session 6 04-01-20

    • Free time on project.

Evaluation

The evaluation is associated to the DEFI-IA

Objective

You will be evaluated on your capacity of acting like a Data Scientist, i.e.

  • Handle a new dataset and explore it.
  • Find a solution to address the defi's problem with a high score (above baseline).
  • Explain the choosen algorithm.
  • Write a complete pipeline to easily reproduce the results.
  • Justify the choice of the algorithms and the environment (CPU/GPU, Cloud etc..).
  • Share it and make your results easily reproducible (Git - docker, conda environment.).

Notations

  1. Project - (60%): a Git repository.

    • The git should contain a clear markdown Readme, which describes (33%)
      • Which result you achieved? In which computation time? On which engine?
      • What do I have to install to be able to reproduce the code?
      • Which command do I have to run to reproduce the results?
    • The code has to be easily reproducible. (33%)
      • Packages required has to be well described. (a requirements.txt files is the best)
      • Conda command or docker command can be furnish
    • The code should be clear and easily readable. (33%)
      • Final results can be run in a script and not a notebook.
      • Only final code can be found in this script.
    • Deadline : January 29 2021.
  2. Rapport - (40%) 10 pages maximum:

    • Quality of the presentation. 25%
    • In-Deep explanation of the chosen algorithm. 25%
    • Choice of the tools-infrastructure used. 25%
    • Results you obtained. 25%
    • Date : January 29, 2021.

Other details

  • Group of 4 to 5 people (DEFI IA's team).

Technical requirements.

All the libraries required for these modules are listed in the requirements.txt (IN CONSTRUCTION/ ONLY SESSION 1 IS OK)

To build a functional environment in pandas execute the following lines:

conda create -n AIF python=3.8
conda activate AIF
pip install -r requirements.txt 
jupyter labextension install jupyterlab-plotly@4.12.0

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