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

   • Cloud computing with Google Cloud,
   • Container with Docker,

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

   • Text.
     • Algorithms: Text processing, Vectorizer, Words Embedding, RNN
     • Libraries : Nltk, Scikit-Learn, Gensim
   • 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.

• R Tutorial
• Python Tutorial
• Elementary statistic tools
• Data Exploration and Clustering.
• Machine Learning
• High Dimensional & Deep Learning

• 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

  • Text: Text Cleaning + Text Vectorization
    • Slides / TP / Video
  • Text: Words Embedding.
    • Slides / TP / Video

• Session 2 - 16-11-20

  • Text: Recurrent Network
    • Slides / TP / Video
  • Development for Data Scientist: Python environment + Github Repo + Python Script.
    • Slides / TP / Video

• Session 3 - 30-11-20

  • Development for Data Scientist: Introduction to Google Cloud Computing.
    • Slides / TP / Video
  • Development for Data Scientist: Docker
    • Slides / TP / Video

• 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

  • Introduction to deep Reinforcement learning: PG Gradient
    • Slides / TP / Video
  • Recommendation System.
    • Slides / TP1 & TP2 / Video

• Session 6 04-01-20

  • Free time on project.

The evaluation is associated to the DEFI-IA

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.).

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.

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

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 [email protected]