Moshe Sipper
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  • Teaching
  • Blog
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Resources (evolutionary algorithms, machine learning, deep 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
  • 10 Popular Machine Learning Algorithms In A Nutshell
  • 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
  • M. Mohri, A. Rostamizadeh, and A. Talwalka, Foundations of Machine Learning, MIT Press, 2012 (freely downloadable)
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) 
  • PyTorch (deep networks)
  • Best-of Machine Learning with Python​
  • Fundamental concepts of PyTorch through self-contained examples​
  • Faster Python calculations with Numba
Datasets
  • Tabular & cleaned (PMLB)
  • By domain
  • ​By application
  • Search engine
  • Kaggle competitions
  • OpenML​
  • UCI Machine Learning Repository
  • ​Image Databases
  • AWS Open Data Registry
  • ​Wikipedia ML Datasets
  • The Big Bad NLP Database
  • ​Datasets for Machine Learning and Deep Learning
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