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• Introduction.
• In-depth Python.
• Statistics for data scientists.
• Rituximab project
• Regression

The practical work below is divided into sessions. You are asked, each time, to read the content, to do the exercises when there are some, and to ask me questions when something is not understood. At the end of each session, you will have to send your production to: cguyeux@femto-st.fr.

At the end of the course, I will randomly draw two practical works per student, to give a final grade on the practical part. The last session will be the occasion to make a final test on paper, covering all the sessions.

First session: introduction to Python

• first steps in Python
• if, for et while
• some predefined methods
• functions
• lists
• string
• exercises to train
• other exercices (online)

Second session : The Pandas library

• Introduction
• Exercices to train
• A few words about the Scikit-Learn library

Third session : Clustering (Unsupervised learning)

• Clustering technics
• Clustering evaluation
• Clustering project

Fourth session : Supervized learning

General presentation

• Automatic learning
• A first classification via SVM
• Other learnings

Supervized learning preparation

• Data preparation
• Dimentionality reduction
• Feature selection

Project

Take your clustering codes from the previous session, and add a dimentionality reduction step upstream. Try several methods, and improve your clustering results according to various metrics previously proposed.

fifth session

• Supervised learning
• Tree based regression
  • Decision tree: implementation
  • Ensemblist methods
• Neuron based regression: cf. below.
• Regression project.

Sixth session: time series

Choose a time series from the UCI directory and use it as an example in the following points. You can send me at the end of the day what you have achieved on the time series of your choice, both on its analysis and its prediction.

• Generalities on time series
  • Introduction
  • Data preparation, visualization
  • Autocorrelation
  • Resampling and interpolation
  • Transformation of observations
  • Smoothing by moving average
• Temporal structures
  • Reference series
    • White noise
    • The random walk
  • Decomposition of time series
    • The decomposition
    • Removing or using a trend
    • Removing or using seasonality
    • Stationarity in time series
• Time series prediction
  • Evaluation of models
  • The autoregression predictive model
  • The sliding average predictive model
  • The ARIMA model

Machine Learning for ioT - M1 IoT

• An introduction to artificial intelligence and machine learning
  • Slides
  • Add-on (link and access Colab Notebook)
    • The code of the gradient descent and simulated annealing to complete and solution.
• A single neuron linear classifier
  • Slides
  • Add-on (link and access Colab notebook)
    • The code of the linear classifier to complete.
• Discovery of Reinforcement Learning
  • Slides
  • Add-on (link and access Colab notebook) to complete or study
    • proj1 - Gym.ipynb
    • proj2 - Empty - TaxiCar-QL.ipynb and Completed - TaxiCar-QL.ipynb
    • proj3 - Empty - NChain-RL.ipynb and Completed - NChain.RL.ipynb
• Machine Learning using scikit-learn
  • Add-on (link and access Colab notebook) to read and complete
    • The basics to complete
    • Decision Trees and Random Forests to complete
• Evaluation : slides to read