Bookshelf

Here is a collection of free books online that I find useful:

Computer Science

• Sedgewick, R., & Wayne, K. (2016). Computer science: An interdisciplinary approach. Addison-Wesley Professional.
• Sedgewick, R., & Wayne, K. (2014). Algorithms. Addison-Wesley Professional.

Machine Learning/Statistics

• Sammut, C., & Webb, G. I. (2017). Encyclopedia of machine learning and data mining. Springer.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
• Lovric, M. (2011). International Encyclopedia of Statistical Science. Springer.
• Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning. New York: Springer series in statistics.
• White, Andrew D (2021). Deep Learning for Molecules and Materials.

Reinforcement Learning

• Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.

Model Interpretability

• Molnar, C. (2019). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable.

Natural Language Processing

• Jurafsky, D., & Martin, J. H. (2009). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Prentice Hall series in artificial intelligence.
• Dale, R., Moisl, H., & Somers, H. (2000). Handbook of natural language processing. CRC Press.

Coding in Python

• Miller, B. N., & Ranum, D. L. (2011). Problem Solving with Algorithms and Data Structures Using Python. SECOND EDITION. Franklin, Beedle & Associates Inc.