Please find below my repository of data science, machine learning and statistics resources. Software-specific material has to a large extent been excluded as these have their respective overviews (R Resources; Python Resources). If you have additions to the list below, please comment or contact me!

##### LAST UPDATED: 31-08-2017

### Courses:

- Introduction to Probability: Part 1 – the Fundamentals (edX & MIT)
- Introduction to Statistical Learning (Hastie & Tibshirani, 2014)
- Coursera: Machine Learning (Stanford)
- Coursera: Applied Data Science with Python (Michigan)
- Coursera: Applied Machine Learning in Python (Michigan)

### Books:

- Machine Learning, Neural and Statistical Classification (Michie, Spiegelhalter, & Taylor, 1994)
- Introduction to Machine Learning (Nilsson, 1998)
- Elements of Statistical Learning (Hastie, Tibshirani, & Friedman, 2001)
- Information Theory, Inference, and Learning Algorithms (MacKay, 2003)
- Gaussian Processes for Machine Learning (Rasmussen & Williams, 2006)
- Introduction to Machine Learning (Shashua, 2008)
- Introduction to Statistical Learning (James, Witten, Hastie, & Tibshirani, 2013)
- Bayesian Reasoning and Machine Learning (Barber, 2016)
- https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf
- A Course in Machine Learning (Daumé III, 2017)
- R for Data Science (Grolemund & Wickham, 2017)