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autonomous_systems_ml

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README.md

SICOM 3A and Master SIGMA program: Statistical/Machine learning course

News

  • For the interested students, we can find here two demo/tutorial notebooks on deep learning for image classification (convolutional neural nets) with the tensorflow 2.X platform and Keras API.
  • For students who have to stay at home for health reasons, and only those who can't attend the face-to-face course, there is a zoom link (see the chamilo page of the course) to participate in videoconference to the class every monday from 15:45 to 17:45.
Lab6 instructions (Monday, October 19)
  • See the notebooks in the 7_clustering folder.
  • Upload at the end of the session your lab 6 short report in the chamilo assigment task (pdf file from your editor, or scanned pdf file of a handwritten paper; code, figures or graphics are not required)
Homework for Monday, October 19
  • Finish reading/understanding the notebooks you didn't cover in the previous lab session.
  • read the lesson (slides) on clustering (unsupervised classification): read up to kernel K-means on slide 34
  • prepare your questions for the course/lab session!
Lab5 instructions
  • Lab5 statement is here
  • upload at the end of the session your lab 5 short report in the chamilo assigment task (pdf file from your editor, or scanned pdf file of a handwritten paper; code, figures or graphics are not required)
Homework for Monday, October 12
  • Finish reading/understanding the notebooks you didn't cover in the previous lab session.
  • read the lesson (slides) on support vector machines:
    • in first reading you can skip the slides 10 to 18 (on constrained convex optimization),
    • the introduction to random forest (appendix, slides 43 to 47) is optional
  • prepare your questions for the course/lab session!
Lab4 instructions
  • Lab4 statement is here
  • upload at the end of the session your lab 4 short report in the chamilo assigment task (pdf file from your editor, or scanned pdf file of a handwritten paper; code, figures or graphics are not required)
Homework for Monday, October 5
  • Finish reading/understanding the notebooks you didn't cover in the previous lab session.
  • read the lesson (slides) on lasso and logistic regression: read up to end of the slides
  • prepare your questions for the course/lab session!
Lab3 instructions
  • Lab3 statement is here
  • upload at the end of the session your lab 3 short report in the chamilo assigment task (pdf file from your editor, or scanned pdf file of a handwritten paper; code, figures or graphics are not required)
Homework for Monday, September 28
  • read the lesson (slides) on linear models: read up to ridge regression slide 23
  • prepare your questions for the course/lab session!
Lab2 instructions
  • Lab2 statement is here
  • upload at the end of the session your lab 2 short report in the chamilo assigment task (pdf file from your editor, or scanned pdf file of a handwritten paper; code, figures or graphics are not required)
Homework for Monday, September 21
  • read the lesson (slides ) on generative models: discriminant analysis + naïve Bayes
  • prepare your questions for the course/lab session!
Lab1 instructions
  • Lab1 is scheduled on Monday 14 for group 1 (15:45 - TP102 Campus), and Tuesday 15 for group 2 (13:30 - Z102 Minatec)
  • Statement is here
  • upload your lab 1 short report in the chamilo assigment task (pdf file from your editor, or scanned pdf file of a handwritten paper; code, figures or graphics are not required)
Homework before the second course on Monday, September 14
  • read and run the introduction notebooks N1_Linear_Classification.ipynb and N2_Polynomial_Classification_Model_Complexity.ipynb
  • answer the questions of the notebook exercises and upload it (pdf file from your editor, or scanned pdf file of a handwritten sheet) under chamilo in the assignment tool (those and only those who do not yet have an agalan account can send it to me by email):
    • only text explanations are required, no need to copy/paste figure or graphics!
    • must not exceed half a length of A4 paper
first course session will take place Monday afternoon, September 7 at Minatec (face-to-face).

Welcome to the Statistical Learning course!

You will find in this gitlab repository the necessary material for the teaching of Machine Learning:

  • course materials for the lessons (slides)
  • examples and exercises for the labs in the form of Jupyter python notebooks (.ipynb files) and/or via online applications,
  • quiz: online tool Socrative, room MLSICOM

These resources will be updated as the sessions progress.

How to use the notebooks?

The examples and exercises will be done under python 3.x through scikit-learn, and also tensorflow. These are two of the most widely used machine learning packages.

The Jupyter Notebooks (.ipynb files) are programs containing both cells of code (for us Python) and cells of markdown text for the narrative side. These notebooks are often used to explore and analyze data. Their processing is done with a jupyter-notebook, or juypyter-lab application, which is accessed through a web browser.

In order to run them you have several possibilities:

  1. Download the notebooks to run them on your machine. This requires a Python environment (> 3.3), and the Jupyter notebook and scikit-learn packages. It is recommended to install them via the anaconda distribution which will directly install all the necessary dependencies.

Or

  1. Use the mybinder service ans links to run them interactively and remotely (online): Binder (open the link and wait a few seconds for the environment to load).
    Warning: Binder is meant for ephemeral interactive coding, meaning that your own modifications/codes/results will be lost when your user session will automatically shut down (basically after 10 minutes of inactivity)

Or

  1. Use a jupyterhub online service:
  • we recommend the UGA's service, jupyterhub.u-ga.fr, so that you can run your notebooks on the UGA's computation server while saving your modifications and results. Also useful to launch a background computation (connection with your Agalan account; requires uploading your notebooks+data to the server).
  • alternatively you can use an equivalent jupyterhub service. For example the one from google, namely google-colab, which allows you to run/save your notebooks and also to share the edition to several collaborators (requires a google account and upload your notebooks+data in your Drive)

Note : You will also find among the notebooks an introduction to Python notebooks/0_python_in_a_nutshell

Miscellaneous remarks on the materials

  • The slides are designed to be self-sufficient (even if the narrative side is often limited by the format).
  • In addition to the slides and bibliographical/web references, we generally propose links or videos (at the beginning or end of the slides) specific to the concepts presented. These lists are of course not exhaustive, and you will find throughout the web many resources, often pedagogical. Feel free to do your own research.