Resources (evolutionary algorithms, machine learning)
Reads / Vids
- Genetic and Evolutionary Algorithms and Programming
- גיא כתבי - אלגוריתמים אבולוציוניים (YouTube) [גיא בוגר הקורס שלי: אלגוריתמים אבולוציוניים וחיים מלאכותיים]
- Introduction to Evolutionary Computing (course/book slides)
- John Koza Genetic Programming (YouTube)
- Some reports in the popular press
- Why video games are essential for inventing artificial intelligence
- Biologic or “By Ole Logic”
- 26 Top Machine Learning Interview Questions and Answers: Theory Edition
- StatQuest with Josh Starmer
- Machine learning preparatory week @PSL
- Neural Networks and Deep Learning (coursera)
- ROC-AUC
- Tinker With a Neural Network in Your Browser
- Common Machine Learning Algorithms for Beginners
- Machine Learning Glossary
- ML YouTube Courses
Books
- M. Sipper, Evolved to Win, Lulu, 2011. (freely downloadable)
- M. Sipper, Machine Nature: The Coming Age of Bio-Inspired Computing, McGraw-Hill, New York, 2002.
- A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, 1st edition, 2003, Corr. 2nd printing, 2007.
- R. Poli, B. Langdon, & N. McPhee, A Field Guide to Genetic Programming, 2008. (freely downloadable)
- J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.
- S. Luke, Essentials of Metaheuristics, 2010. (freely downloadable)
- A. Geron, Hands On Machine Learning with Scikit Learn and TensorFlow
- G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning, 2nd edition, 2021 (freely downloadable)
- J. VanderPlas, Python Data Science Handbook
- K. Reitz, The Hitchhiker’s Guide to Python
- M. Nielsen, Neural Networks and Deep Learning
- Z. Michalewicz & D.B. Fogel, How to Solve It: Modern Heuristics, 2nd ed. Revised and Extended, 2004.
- Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin, 3rd edition, 1996.
- D. Floreano & C. Mattiussi, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, MIT Press, 2008.
- A. Tettamanzi & M. Tomassini, Soft Computing: Integrating Evolutionary, Neural, and Fuzzy Systems, Springer-Verlag, Heidelberg, 2001.
Software
- gplearn: Genetic Programming in Python, with a scikit-learn inspired and compatible API
- LEAP: Library for Evolutionary Algorithms in Python
- DEAP: Distributed Evolutionary Algorithms in Python
- Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm, Artificial Fish Swarm Algorithm in Python)
- Scikit-learn: Machine Learning in Python
- Mlxtend (machine learning extensions)
- Best-of Machine Learning with Python
- Fundamental concepts of PyTorch through self-contained examples