Supported by the Foundational Questions Institute, a new MOOC (massive open online course) on the new and exciting field of "Algorithmic Information Dynamics" will be released on June 12ve by the Santa Fe Institute. The course offers a novel computational perspective to causality and living systems, from complex networks to reprogramming cells. You are all welcome to sign up for the course, for a small fee with access to certificate and materials, or to watch it for free.
You can learn more about the MOOC from this brief introductory video:
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Harnessing the power of computational models:
Probability and statistics have long helped scientists make sense of data about the natural world -- to find meaningful signals in the noise. But classical statistics prove a little threadbare in today's landscape of large datasets, which are driving new insights in disciplines ranging from biology to ecology to economics. It's as true in biology, with the advent of genome sequencing, as it is in astronomy, with telescope surveys charting the entire sky. The data have changed.
Algorithmic Information Dynamics is an exciting new field put forward by our lab based upon some of the most mathematically mature and powerful theories put together in harmony to tackle some of the challenges of causal discovery from a heavily model-driven and mechanistic perspective.
Taught by me and my friend and colleague Dr. Narsis A. Kiani, co-leaders of the Algorithmic Dynamics Lab, the course will provide a conceptual introduction to the field focusing on mathematical and computational aspects in the study of causality. The course covers key aspects from graph theory and network science, information theory, dynamical systems and algorithmic complexity. It will venture into ongoing research in fundamental science and its applications to behavioral, evolutionary and molecular biology.
After a conceptual overview of the main motivation and some historical developments, we will 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 behavioral, evolutionary and molecular biology, in particular genetic networks.
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. Foundations of Algorithmic Information Dynamics and Reprogrammability
7. Applications to Behavioural, Evolutionary and Molecular Biology
Tuition is $50, which is required to get to the course material during the course and a certificate at the end. But the course will also be made available free to watch and if no fee is paid materials will not be available until the course closes. Donations are highly encouraged and appreciated in support for SFI's ComplexityExplorer to continue offering new courses.
In addition to all course materials tuition includes:
• Six-month access to the Wolfram|One platform (renewable for other six)
• Free digital copy of the course textbook to be published by Cambridge University Press
• Several gifts will be given away to the top students finishing the course, check the FAQ page for more details.
You can register yourself at here.
We look forward to you doing us!
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Hector Zenil is co-leader of the Algorithmic Dynamics Lab, and a member of FQXi.