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Algorithmic Information Dynamics: From Networks to Cells

Santa Fe Institute via Complexity Explorer

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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 for Complexity Explorer's Algorithmic Information Dynamics: From Networks to Cells
4.6 Based on 59 reviews

  • 5 stars 80%
  • 4 stars 12%
  • 3 stars 3%
  • 2 stars 3%
  • 1 star 2%

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  • 1
Anonymous
5.0 2 months ago
Anonymous completed this course.
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 courses give you what it is already known and can be found in hundreds of books, evidently just transmitting old knowledge may give you the impression that everything is established and error-free.

The course has been one of the most successful courses in the Santa Fe Complexity Explorer (by all standards, including the number of students …
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Anonymous
3.0 2 months ago
Anonymous completed this course.
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 testing these concepts? It was hard to tell what content was actually important and what was fluff. Some lectures were entirely superfluous--maybe less important lectures could be labeled *optional* so busy students can figure out how best to allocate their time.

The actual "meat" of the course was introduced too late. There…
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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!
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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 can actually watch the videos online for free.

This is an amazing course, not for everyone if you are looking the average MOOC course based on an old topic given by some teacher that has given the same course 100 times in their lives.

Those focusing on minor typos here and there are insane and missing the big picture. EVEN T…
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Pablo S
5.0 2 months ago
Pablo completed this course, spending 10 hours a week on it and found the course difficulty to be very hard.
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 established with a lot of previous experiences that have let them correct common and particular mistakes that come with teaching such a new subject. The second type is what I experienced in the Algorithmic Information Dynamics: From Networks to Cells course, where they made a lot of effort to give us access to an ongoing project and a book that is o…
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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.
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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, because it really seems to open up a new world of possibilities with respect to data analysis and model inference.

As much as it pains me, and even considering that it was the first time the course was given, it must be admitted that the development of the course was a little messy. Indeed, the original plan (including the evaluation system) …
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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 does not end, programs seen as a string of bits .... expressions in which words program and algorithm are renewed: when the words program or algorithm appear, my mind went through COBOL code, payroll or inventory management systems and asked "can I think of the complexity of a payroll system?, can I measure it?, in such a system, which is s?, which i…
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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 mechanics and information theory.

Well, this course is everything except about old stuff, the only old stuff they teach are preliminaries that you will need to understand the main part of the course. But you have for sure never seen online course on computability theory, algorithmic probability or Kolmogorov randomness, let alone the areas on…
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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, something that everybody does without getting into details.

The authors will explain you why you should not rely in popular compression algorithms to approximate Kolmogorov complexity.

For the first time, I have been able to get a real grasp on Shannon entropy and its many limitations, the instructors keep reminding you…
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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, but that is something that I plan to look into and I have several ideas that I will be posting on the forum in due course. I will also be making some of my colleagues in the petroleum industry in Calgary aware of AID as there is a lot of effort going into the analysis of information to which it could be applied.

It cover…
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Anonymous
4.0 2 months ago
Anonymous completed this course.
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 within the videos themselves, but I think that it would give a smoother feel to the course overall. Let me be a bit more precise: A lot of the material covered in the sections building up to section 6 wasn't strictly necessary, or at least seemed somewhat detached in the form of it being a subject for a whole other course in itself. In the spirit o…
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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 medicine and artificial intelligence, where new techniques are required beyond purely statistical methods in order to reprogram and understand behavior of adaptive systems. Moreover, the use of Wolfram Language enhanced learning by providing a transparent way of modelling real world phenomena and testing new ideas. Overall, I enjoyed the course and it motivated me to apply AID in my masters' thesis: birdsong analysis. I strongly recommend teachers to keep improving / sharing / creating new study materials and keep contact with students. I am sure eventually every field will find an interesting use for AID. Maybe in some years there will be enough projects for an International Conference.
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Anonymous
1.0 3 months ago
Anonymous is taking this course right now.
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 to speak, but it is not delivering much more than a one dollar bill.

It gathered some great students from around the world. Indeed. This is an interesting point in its favour, but just browse into its forum, and you will see the many dozens (!) of errors that they spotted in the lectures and other materials.

T…
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Joe S
5.0 2 months ago
Joe completed this course.
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!!!
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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.
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Letourneau F
5.0 2 months ago
Letourneau completed this course.
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.
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Anonymous
5.0 2 months ago
Anonymous completed this course.
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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Anonymous
5.0 2 months ago
Anonymous completed this course.
I enjoyed this course a lot. It didn't bother me that the material was delivered with uneven timing. The instructors were clearly putting the lectures together as the class was going on. Because much of the material was far outside my usual study area, I enjoyed the rather elementary beginning. My Wolfram Language skills are pretty good, so I also enjoyed the coding examples. I also appreciated the participants providing some examples of Wolfram code. I learned a lot.
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