- Concrete Mathematics: A Foundation for Computer Science (great generic initial approach)
- Introduction to Linear Algebra, Fifth Edition (2016) (more formal approach)
- Calculus Made Easy - Silvanus P. Thompson
- Numerical Linear Algebra
- Numerical Methods
- https://www.amazon.com/Mathematics-Elementary-Approach-Ideas-Methods/dp/0195105192 TOCHECK
- https://www.amazon.com/Basic-Mathematics-Serge-Lang/dp/0387967877 TOCHECK
- Think Stats 2e - Allen B. Downey (Python, good intuitive explanation of basic concepts, often from a different POV compared to other more technical books about statistics)
- Think Bayes (Python, many good concrete examples that can be easily implemented and solved from scratch, greatly helps to absorb Bayes's Theorem and related concepts at a more intuitive level. A bit of interdependency between chapters with code written/presented in previous ones)
- OpenIntro Statistics 3rd Edition (exercises and inline questions with solutions)
- An Introduction to Statistical Learning (R)
- The Elements of Statistical Learning 2 (math)
- Probability Theory: The Logic of Science TOREAD
- Time Series Analysis - Hamilton
- Introductory Time Series with R (Use R!) - Cowpertwait and Metcalfe
- Time Series Analysis and Its Applications: With R Examples - Shumway and Stoffer
- Introduction to Time Series and Forecasting - Brockwell and Davis
- Machine Learning - Tom Mitchell - 1997
- Artificial Intelligence: A Modern Approach 3rd Edition
- Pattern Recognition and Machine Learning by Christopher M. Bishop TOREAD
- Applied Predictive Modeling TOREAD
- Model-Based Machine Learning TOCHECK
- Python Machine Learning - Sebastian Raschka TOREAD
- deeplearningbook (math, also overview and details about many technical prerequisites to machine learning, like linear algebra, probability and alike)
- neuralnetworksanddeeplearning (great intuitive look into deep learning models and generic machine learning techniques)
- Deep Learning with Python - François Chollet TOREAD
- Python Cookbook, 3rd Edition by David Beazley; Brian K. Jone (great collection of advanced recipes, tips and techniques)
- The Pragmatic Programmer: From Journeyman to Master (fantastic overview of best practices and mindset to make life as a programmer as easy, fruitful and pain-free as possible)
- Computational Linear Algebra for Coders (Jupyter, actual implementation + optimization considerations for many major statistical procedures like PCA, SVD, etc.)
- CS231n Convolutional Neural Networks for Visual Recognition
- Practical Deep Learning For Coders, Part 1 (entertaining, great view of SOA results, notebooks and great tips on code)
- Practical Deep Learning For Coders, Part 2 TODO
- Deep Learning by Google @ udacity
- Creative Applications of Deep Learning with Tensorflow (good interactive examples and exposition, some specific topics not generally found in other courses, otherwise usual CNN and RNN pass)
- Generative Art and Computational Creativity TODO
- Deep Learning by Andrew Ng @ Coursera TODO
- Hadoop Platform and Application Framework @ coursera
- Intro to Hadoop and MapReduce @ udacity
- Machine Learning With Big Data @ coursera (brief generic intro to machine learning, practical examples for Spark and KNIME)
- Spark Fundamentals 1 (poorly taught, monotonous reading of slides, non relevant quizzes)