class: center, middle ## Machine learning with Python Pierre Ablin Slides and notebooks on my webpage pierreablin.com/masef --- ## Course organisation 1) Introduction to machine learning + basics of Python 2) Optimization in Python + Numpy 3) Some machine learning algorithms + Scikit-learn 4) Introduction to deep learning + Pytorch 5) Building a deep learning architecture for image recognition --- ## Plan of this lecture - What is machine learning ? A brief history - Broken locks and recent successes - Computer vision - Text analysis - Speech analysis - Game playing - Human behavior modelling --- ## What is machine learning ? - Term that has become common all the way up to general media (since 2015) - Often heard along the words - "Artificial Intelligence"/"Deep learning" - A century-old concept that I'll try to introduce --- ## What is a machine ? .center[
] Standard *computer science* paradigm. Program a.k.a. algorithm
Ada Lovelace
Von Neumann
Alan Turing
Edsger Dijsktra
--- ## How do we learn ? .center[
] .center[**Learning by trial and error**] --- ## How do we learn ? .center[
] .center[**Learning by organizing new knowledge**] --- ## How do we learn ? .center[
] .center[**Learning by comparison with a reference**] --- ## How can a machine learn ? Supervised learning process .center[
] Implementing a program that learns from supervision .center[
] --- ## Machine learning as a field - A subfield of "artificial intelligence" (computer science), where *intelligence* is acquired from *data* .center[
] - Extract information from data: relies on statistics - Training relies on optimization and mathematics --- ## The new boom explained - Computational power .center[
] - Powerful programming frameworks enabling fast research and deployment .center[
] Python at the heart of Machine learning research --- ## The new boom explained - Available datasets and growing data corpus - Money and people .center[
] - Private funds from the GAFAM, public funding from Chinese governement --- ## Machine learning is now everywhere 1. Computer vision 2. Speech processing 3. Text understanding 4. Game playing 5. Behavior modelling --- ## Image processing .center[
] --- ## Image processing .center[
] .credits[Slide courtesy of https://github.com/m2dsupsdlclass/lectures-labs] --- ## Image processing .center[
] .credits[Slide courtesy of https://github.com/m2dsupsdlclass/lectures-labs] --- ## Text processing .center[
] --- ## Text processing .center[
] .credits[Slide courtesy of https://github.com/m2dsupsdlclass/lectures-labs] --- ## Text processing .center[
] .credits[Slide courtesy of https://github.com/m2dsupsdlclass/lectures-labs] --- ## Text/speech processing - GPT-3 [2020] requires only a few examples of a task to generalize. [example](https://pic.twitter.com/HFjZOgJvR8) .center[
] --- ## Combining modalities .center[
] .credits[Slide courtesy of https://github.com/m2dsupsdlclass/lectures-labs] --- ## Generating false data .center[
] --- ## Generating false data .center[
[StyleGAN2 2018] ] .center[
[WaveNet 2017] ] --- ## Generating false data
--- ## ML for other sciences .center[
] --- ## ML for other sciences .center[
] --- ## Playing games .center[
] --- ## Playing games .center[
] --- ## In this course - We will learn to use Python - The goal is to build a system to perform image recognition, from scratch !