Complex Systems, Neural Networks.

DRAFT - WORK IN PROGRES …

Important Note: This page is closely related to PROBABILITY, CHAOS AND FRACTALS in the recommended reading lists in Mathematics, they should therefore be consulted together.

  • John Holland - Complexity Very short introduction

  • Melanie Mitchell - Complexity. a Guided Tour

  • M. Mitchell Waldrop - Complexity. the Emerging Science at the Edge of Order and Chaos

  • Simon Levin - Fragile Dominion. Complexity And The Commons

  • Albert-laszlo Barabasi - Linked: How Everything Is Connected to Everything Else transl.it. “Link”

  • Steven H. Strogatz - SYNC The Emerging Science of Spontaneous Order

  • Geoffrey West - Scale: The Universal Laws of Life, Growth, and Death in Organisms, Cities, and Companies

  • Duncan J. Watts - Small Worlds: The Dynamics of Networks Between Order and Randomness (2004)

  • Duncan J. Watts - Six Degrees: The Science of a Connected Age

  • Guido Caldarelli, Michele Catanzaro - Networks. A very short introduction (2012) transl.it. network science

  • A. Vespignani - The algorithm and the oracle: how science predicts the future and helps us change it

  • Giorgio Parisi - As in a Flight of Starlings

  • James Ladyman, Karoline Wiesner - What is a Complex System? (2020)

COMPLEX SYSTEMS: INSIGHTS

  • Hiroki Sayama - Introduction to the Modeling and Analysis of Complex Systems-Open SUNY Textbooks

  • Stefan Thurner, Rudolf Hanel, Peter Klimek - Introduction to the Theory of Complex SystemsP (2018)

  • Sergey Dorogovtsev - Lectures on Complex Networks

  • Mark Newman, Duncan J. Watts, Albert-László Barabási - The Structure and Dynamics of Networks (2013)

  • Guido Caldarelli -Scale-Free Networks: Complex Webs in Nature and Technology (2013)

  • Alain Barrat, Marc Barthélemy, Alessandro Vespignani - Dynamical Processes on Complex Networks

  • Allen Downey - Think Complexity (2nd ed.)

  • Herbert Simon - The Science of the Artificial

Online Resources:

NEURAL NETWORKS AND MACHINE LEARNING

  • Brunello Tirozzi - Mathematical Models of Neural Networks - Padua, 1995

  • D.J.C. Mac Kay - Information Theory, Inference and Learning Algorithms

  • C.M. Bishop - Pattern Recognition and Machine Learning

  • Mehta et al. - A high-bias, low-variance introduction to Machine Learning for physicists

  • A.C.C. Coolen, R. Kuhn, P. Sollich - Theory of Neural Information Processing Systems

  • Simon O. Haykin - Neural Networks and Learning Machines

  • I. Goodfellow, Y. Bengio, A. Courville - Deep Learning

  • A. Geron - Hands-On Machine Learning With Scikit-Learn and Tensorflow