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Santa Fe Institute

Algorithmic Information Dynamics: From Networks to Cells

Santa Fe Institute via Complexity Explorer

This course may be unavailable.

Overview

This course provides a conceptual introduction to the new and exciting field of Algorithmic Information Dynamics focusing on mathematical and computational aspects in the study of causality. To this end, the course first covers key aspects from graph theory and network science, information theory, dynamical systems and algorithmic complexity in a tour the force to finally tackle causation from a model-driven approach removed from traditional statistics and classical probability theory. The course will venture into ongoing research to show exciting new avenues to uncharted territory.

It is desirable that students have some idea of basic mathematics but optional modules will be provided in a parallel track. Also desirable is some computer programming skills, but also some basics of the Wolfram Language and a 6-month access to Wolfram|One (Mathematica) will be granted (extendable by other 6 months). However, the course does not require you to adopt any particular programming language nor it requires one.

Because of its nature, the course is aimed to a wide range of possible students who have had some basic knowledge of college-level math or physics to active researchers seeking to take advantage of new tools for algorithmic data science beyond traditional machine learning.

After a conceptual overview of the main motivation and some historical developments, we review some preliminary aspects needed to understand the most advanced topics. These include basic concepts of statistics and probability, notions of computability and algorithmic complexity and brief introductions to graph theory and dynamical systems. We then dig deeper into the core of the course, that of Algorithmic Information Dynamics which brings all these areas together in harmony to serve in the challenge of causality discovery, the most important topic in science. Central to the course and the field is the theory of algorithmic probability that establishes a formal bridge between computation, complexity and probability.

Finally, we move towards new measures and tools related to reprogramming artificial and biological systems, applications to biological evolution, evolutionary programming, phase space and space-time reconstruction, epigenetic landscapes and aspects relevant to data analytics and machine learning such as model generation, feature selection, dimensionality reduction and causal deconvolution. We will showcase the tools and framework in applications to systems biology, genetic networks and cognition by way of behavioural sequences. Because of the wide scope of application students will be able apply the tools to their own data and own problems as we will be explaining how to do it in detail, and we will be providing all the tools and code for it.

Throughout the course, students will be given assignments that will go from the conceptual to the mathematical and computational intended to keep everybody engaged.

About the Instructor(s):
Hector Zenil has a PhD in Computer Science from the University of Lille 1 and a PhD in Philosophy and Epistemology from the Pantheon-Sorbonne, University of Paris. He co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden. He is also the head of the Algorithmic Nature Group, the Paris-based lab that started the Online Algorithmic Complexity Calculator and the Human Randomness Perception and Generation Project. Previously, he was a Research Associate at the Behavioural and Evolutionary Theory Lab at the Department of Computer Science at the University of Sheffield in the UK before joining the Department of Computer Science, University of Oxford as a faculty member and senior researcher.

Narsis Kiani has a PhD in Mathematics and has been a postdoctoral researcher at Dresden University of Technology and at the University of Heidelberg in Germany. She has been a VINNOVA Marie Curie Fellow in Sweden and co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden.

Hector and Narsis are co-leaders of the Algorithmic Dynamics Lab at the Unit of Computational Medicine at Karolinska Institute.

Course Team:
Antonio Rueda-Toicen has an MSc degree in Bioengineering and a Licentiate degree in Computer Science. He is an instructor and researcher at Instituto Nacional de Bioingeniería (INABIO) at Universidad Central de Venezuela and is a Research Programmer at the Algorithmic Dynamics Lab.

Syllabus

  1. A Computational Approach to Causality
  2. Technical Skills and Selected Topics
  3. A Brief Introduction to Graph Theory and Biological Networks
  4. Basics of Computability, Information Theory and Algorithmic Complexity
  5. Dynamical Systems as Models of the World
  6. Algorithmic Information Dynamics
  7. Applications to Behavioural, Evolutionary and Molecular Biology

Taught by

Narsis Kiani and Hector Zenil

Reviews

4.5 rating, based on 61 Class Central reviews

Start your review of Algorithmic Information Dynamics: From Networks to Cells

  • Anonymous
    This is the first time this course is given and the instructors didn't play it safe as they advertized. They delivered a masterful class on how research is and should be conducted and how students' intelligences can be treated with respect. Most cou…
  • Anonymous
    It was okay. The material was interesting but the course structure and scheduling need work. There was too much time dedicated to prerequisite information. If this stuff is so important for understanding the later ideas, why was there no midterm t…
  • Anonymous
    I cannot praise more this course, it has changed the way I see the world.

    People interested in digital physics should be fascinated because it finally delivers on the promise, you can reprogram the world and the authors have shown how to do it, they have found new universal Turing machines with their tools and shown how they can reprogram almost every program and then they show you how to go and look for the most likely programs generating data.

    Those programs are generating mechanisms hence, as the authors put it, right in the realm of the fundamental challenge of science: causation!
  • Joe Sauvageau
    This excellent class, taught by Dr. Hector Zenil, the pioneer in the field of Algorithmic Information Dynamics, was quite informative and inspiring. The course provided a very good review of the background required in order to appreciate the work that has been accomplished in the field and the challenges ahead. AID is making revolutionary strides across multiple areas such as behavioral sequences, cognition, evolutionary biology, molecular biology, genetic engineering, and machine learning. All material is nicely pulled together in Unit #6 with inspiring applications demonstrated in Unit #7. A highly recommended course! Thanks to Dr Zenil and the AID Team!!!
  • Anonymous
    I could not have asked more, I came with high expectations and this course was even more than what I expected. The instructors are not paid and the platform requests a 50 USD donation that goes to the Santa Fe Institute for admin purposes but you ca…
  • Profile image for Pablo Sanchez
    Pablo Sanchez
    I'm taking a couple of courses from Complexity Explorer of the Santa Fe Institute and they are a great way to have access to technics and knowledge at the forefront of science. Thus there two types of courses , first it would be the courses already…
  • Anonymous
    I can see how this research program on AID can help and revolutionze again the field of artificial intelligence. The first course on algorithmic information theory and algorithmic probability, fundamental topics that should be in the standard curricula of a computer science degree, or basically any degree related to science, because as the instructors explain, it is the fndamental theory of inference, so it cannot get more relevant.
  • Anonymous
    Terribly fascinating course about a very, very intriguing and novel area of science. In fact some topics covered correspond to papers only a months old. I wholeheartedly recommend it to anyone interested in data-driven science and technology, becaus…
  • Anonymous
    After several years dealing with traditional programing languages BASIC, RPG, COBOL, designing algorithms to solve trivial problems, my thinking has been renewed listening to talk about algorithmic randomness, algorithmic probability, program that d…
  • Anonymous
    This is a fantastic course for anyone who gets a kick from relating ideas from many different domains. Algorithmic Information Dynamics is such a new field that the tutors have done the world a favor by preparing this course while taking time out from their active research.

    -Paras Chopra
  • Anonymous
    Have you ever felt that courses, online or offline, promise you a lot, to be paradigm shift and groundbreaking and then you are left feeling like they are still using and teaching the same old stuff, you know, probability theory, statistical mechani…
  • Anonymous
    Mind blowing stuff in this course. I was interested in Algorithmic Information Theory but I had been unable to find any other course with not even some material in it covering the area beyond giving the definition of Kolmogorov complexity, somethin…
  • Anonymous
    A challenging but fascinating course. I appreciate the enormous effort that Hector, Narsis, and Alyssa, put into it and they have my thanks. Hopefully they can take a few days off, now! I am a geologist but not aware of any AID uses in that field…
  • Anonymous
    Overall, I enjoyed the course – especially the later parts. I have a few suggestions for improvement. To me, it would be worth trying to incorporate the theme in section 6 into all of the material in a clearer fashion. This might mean small detours…
  • Anonymous
    Hector and Narsis did an amazing job at communicating their research and explaining the core of Algorithmic Information Dynamics. The topics disussed during the MOOC will have profound implications in the study of complex systems. Specially in medic…
  • Anonymous
    This course is a tremendous failure (9/9/2018), definitely not worth to be part of the Santa Fe Institute and The Complexity Explorer projects. Very much like what was said about "cold fusion", it promised to pay for the external debt of the USA, so…
  • Anonymous
    This course is a complete chaos. I cannot believe that some people actually think it was okay! The videos and forums are full of mistakes, not to mention that the instructors are hard to understand because of their poor English skills. The lectures…
  • Anonymous
    The world is catching up with these instructors! while science is still in the steam engine era with everything studied from the point of view of a theory more than a hundred years old (Entropy), or 60 years old from its modern formulation by Shannon, Profs. Zenil and Kiani are bringing science to our days of computers and programs in an authoritative way. Even stuff from black holes based on information theory and silly theories like the holographic principle are based on retarded views of information (which is nothing but probability distributions, not really information in any other way). Its time to leave regression and correlation for amateurs, they don't have room in modern science after the work of these instructors.
  • Letourneau Federico
    I usually work with observational data and apply statistical methods to analyze them, furthermore, I work with system dynamics developing models of cause - effects and even teaching postgraduate introductory courses about complex systems in forestry and agriculture. I'm always looking for new ways of understanding the complexity. When the term algorithmic complexity came to my ears I've said what is that!!, so I've enrolled immediately. Now that I finish the course, I can say that I am really surprised by this new way, at least for me, to observe the reality and the natural phenomena, and what is better I have tools and techniques to do it. Thank you, Hector, Narsis, and Allysa, you have done a great job.
  • Anonymous
    Amazing course! Students have gathered around working groups and are now creating a cryptocurrency based on the MOOC topic, are creating a new compression scheme based on a key idea from a method presented in the course, etc! This is absolutely amazing how much excitement it created and generated.

    You should take this course and see by yourself. First units are sometimes a lot to take but are necessary later on, they put everything together, this is how science should be done, truly interdsiciplinary with each part playing an important role and not artificially put together.

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