Papers

• An Approach Based on Bayesian Networks for Query Selectivity Estimation - DASFAA, 2019
• Master 2 year internship at HelloFresh (report, slides)
• Master 1 year internship at Privateaser (report, slides)
• Undergraduate internship at INSA Toulouse (report, slides)
• Detailed solutions to the first 30 Project Euler problems

Talks

I’ve given a few talks at conferences and companies.

• The Benefits of Online Learning - Quantmetry, Paris, 2019
• The Benefits of Online Learning - Element AI, London, 2019
• The Benefits of Online Learning - Airbus BizLab, Toulouse, 2019
• An Approach Based on Bayesian Networks for Query Selectivity Estimation - DASFAA, 2019
• Machine learning incrémental: des concepts à la pratique - Toulouse Data Science, 2019
• Online machine learning with creme - PyData, Amsterdam, 2019
• Docker for data science - HelloFresh, Berlin, 2017
• Challenge Big Data - Toulouse, 2017
• Forecasting bicycle-sharing usage - Toulouse Data Science, 2016

Datasets

• OpenBikes 2016 challenge

Hall of fame

The following is a hall of fame of papers, books, and blog posts that have a very high signal to noise ratio; I highly recommend reading some of them when you get time.

• The Elements of Statistical Learning - Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
• Machine Learning - Tom Mitchell – I think this wonderful textbook is under-appreciated.
• Artificial Intelligence: A Modern Approach - Russel & Norvig
• mlcourse.ai – Of all the introductions to machine learning I think this is the one that strikes the best balance between theory and practice.
• Machine learning cheat sheets - Shervine Amidi
• Kalman and Bayesian Filters in Python - Roger Labbe – Kalman filters are notoriously hard to grok, this tutorial nicely builds up the steps to understanding them.
• CS231n Convolutional Neural Networks for Visual Recognition - Stanford
• Algorithmes d’optimisation non-linéaire sans contrainte (French) - Michel Bergmann
• Graphical Models in a Nutshell - Koller et al.
• Rules of Machine Learning: Best Practices for ML Engineering - Martin Zinkevich – You should read this once a year.
• A Few Useful Things to Know about Machine Learning - Pedro Domingos – This short paper summarizes basic truths in machine learning.
• Choose Boring Technology - Dan McKinley
• How to Write a Spelling Corrector - Peter Norvig – Magic in 36 lines of code.
• MCMC sampling for dummies - Thomas Wiecki
• Your Easy Guide to Latent Dirichlet Allocation
• An Intuitive Explanation of Convolutional Neural Networks - Ujjwal Karn
• An overview of gradient descent optimization algorithms - Sebastian Ruder
• How to explain gradient boosting - Terence Parr and Jeremy Howard – A very good introduction to vanilla gradient boosting with step by step examples.
• Why Does XGBoost Win “Every” Machine Learning Competition? - Didrik Nielsen – This Master’s thesis goes into some of the details of XGBoost without being too bloated.
• Good sleep, good learning, good life - Piotr Wozniak – Extremely long and nothing to do with data science, but a very thorough essay nonetheless on how to properly sleep.
• Make for data scientists - Paul Butler – I believe Makefiles are yet to be rediscovered for managing data science pipelines.
• Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations – Just read it.
• The Cramér-Rao Lower Bound on Variance: Adam and Eve’s “Uncertainty Principle” - Michael Powers
• Kaggle contest on Observing Dark World - Cam Davidson-Pilon – If you’re not convinced about the power of Bayesian machine learning then read this and get your mind blown.
• A Concrete Introduction to Probability (using Python) - Peter Norvig – Extremely elegant Python coding.
• The Hungarian Maximum Likelihood Trick - Louis Abraham
• Machine Learning for Signal Processing - University of Illinois
• Don’t Call Yourself A Programmer, And Other Career Advice
• Tidy Data - Hadley Wickham – If you like playing with data then you need to be aware of this one.
• Gaussian Process, not quite for dummies - Yuge Shi – Gaussian processes are quite difficult to understand (at least, for me) but Yuge gives some great visual intuitions.
• Continuous Delivery for Machine Learning - Martin Fowler
• Memos - Sriram Krishnan
• Frequentism and Bayesianism: A Python-driven Primer - Jake VanderPlas
• A Few Useful Things To Know About Machine Learning - Pedro Domingos
• Multiworld Testing Decision Service: A System for Experimentation, Learning, And Decision-Making

Blogroll

This is a list of blogs I regularly scroll through.

• Tim Salimans on Data Analysis
• Randal Olson
• Sam & Max – French and NSFW!
• Sebastian Raschka
• Clean Coder
• Pythonic Perambulations
• Erik Bernhardsson
• otoro
• Terra Incognita
• Real Python
• Airbnb Engineering
• No Free Hunch
• The Unofficial Google Data Science Blog
• will wolf
• Edwin Chen
• Use the index, Luke!
• Jack Preston
• Agustinus Kristiadi
• DataGenetics
• Katherine Bailey
• Netflix Research
• inFERENce
• Hyndsight – Rob Hyndman is a time series specialist
• While My MCMC Gently Samples
• Ines Montani – By one of the founders of spaCy
• Stephen Smerity
• Peter Norvig
• IT Best Kept Secret Is Optimization – By Jean-Francois Puget, aka CPMP
• explained.ai
• Better Explained
• Genetic Argonaut
• pandas blog
• Towards Data Science
• Linear Disgressions – data science podcasts
• Probably Overthinking It
• Simply Statistics
• Practically Predictable
• koaning – By Vincent Warmerdam, who did this great presentation
• blogarithms
• Possibly Wrong
• FastML
• Parameter-free Learning and Optimization Algorithms