Max Halford

Links

## Papers

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

## Presentations

## Datasets

## Internship hand ins

## 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