Papers

• Cost models in database query optimisation bibliography
• Detailed solutions to the first 30 Project Euler problems

Presentations

• Docker for data science

Datasets

• OpenBikes 2016 challenge

Internship hand ins

• Undergraduate (report, slides)
• First Master’s year (report, slides)

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 and that I thoroughly recommend.

• The Elements of Statistical Learning
• Machine Learning - Tom Mitchell
• Artificial Intelligence: A Modern Approach - Russel & Norvig
• A Few Useful Things to Know about Machine Learning - Pedro Domingos
• Choose Boring Technology - Dan McKinley
• How to Write a Spelling Corrector - Peter Norvig
• Machine learning cheat sheets - Shervine Amidi
• A Gentle Introduction to Gradient Boosting - Cheng Li
• Kalman and Bayesian Filters in Python - Roger Labbe
• MCMC sampling for dummies - Thomas Wiecki
• Rules of Machine Learning: Best Practices for ML Engineering - Martin Zinkevich
• Your Easy Guide to Latent Dirichlet Allocation
• Graphical Models in a Nutshell - Koller et al.
• Semi-Supervised Learning Tutorial - Xiaojin Zhu
• CS231n Convolutional Neural Networks for Visual Recognition - Stanford
• An Intuitive Explanation of Convolutional Neural Networks - Ujjwal Karn
• An overview of gradient descent optimization algorithms - Sebastian Ruder