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Books available online, related to the topics discussed. Continuously updated.
- Deep Learning - Foundations and Concepts, Christopher M. Bishop, Hugh Bishop (pdf)
- Pattern Classification and Machine Learning, Christopher M. Bishop (pdf)
- Model-Based Machine Learning, John Winn et al. (pdf)
- Mathematics for Machine Learning, Deisenroth, Aldo Faisal, Cheng S. Ong (pdf)
- Neuronal Dynamics, Wulfram Gerstner et al. (online)
- The Little Book of Deep Learning, Francois Fleuret (pdf)
- Schaum's Outline of Linear Algebra (6th Ed.), Seymour Lipschutz and Marc Lipson (pdf)
- Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David (pdf)
- Information Theory, Inference, and Learning Algorithms, David MacKay (pdf)
- An Introduction to Optimization (4th editions), Edwin KP Chong, Stanislaw H Zak (pdf)
Material for the course Statistics for data Science at EPFL:
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- probabilistic density, distribution and parameters for continuous and discrete distributions
Other useful resources:
- Latex Mathematical Symbols
- The Matrix Cookbook
- Computing Gradients with Backpropagation (Automatic Differentiation), from the Univ Princeton course COS-324, by Ryan P. Adams (pdf)
- OpenAI's guide to Prompt Engineering