Algorithmic Information Dynamics: A Computational Approach to Causality and Living Systems From Networks to Cells
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Owner/Developer: Complexity explorer - Santa Fe Institute
United States of America
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|Description:||About the Course: 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. Maybe it's time our data analysis tools did, too. During this three-month online course, starting June 11th, instructors Hector Zenil and Narsis Kiani will introduce students to concepts from the exciting new field of Algorithm Information Dynamics to search for solutions to fundamental questions about causality — that is, why a particular set of circumstances lead to a particular outcome. Algorithmic Information Dynamics (or Algorithmic Dynamics in short) is a new type of discrete calculus based on computer programming to study causation by generating mechanistic models to help find first principles of physical phenomena building up the next generation of machine learning. 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.|
Interactive online resources
Optional / Voluntary
Students should have basic knowledge of college-level math or physics, though optional sessions will help students with more technical concepts. Basic computer programming skills are also desirable, though not required. The course does not require students to adopt any particular programming language for the Wolfram Language will be mostly used and the instructors will share a lot of code written in this language that student will be able to use, study and exploit for their own purposes.
Academia, Industry, Governmental bodies, Contract Research Organizations (CROs), Consulting, SMEs
Continuing Professional Development
Partial coverage (e.g. a module)
|Details on the topic or technology covered:||
The course will begin with a conceptual overview of the field.
Then it will review foundational theories like basic concepts of statistics and probability, notions of computability and algorithmic complexity, and brief introductions to graph theory and dynamical systems.
Finally, the course explores new measures and tools related to reprogramming artificial and biological systems. It will showcase the tools and framework in applications to systems biology, genetic networks and cognition by way of behavioral sequences.
Students will be able apply the tools to their own data and problems. The instructors will explain in detail how to do this, and will provide all the tools and code to do so.
A Computational Approach to Causality
A Brief Introduction to Graph Theory and Biological Networks
Elements of Information Theory and Computability
Randomness and Algorithmic Complexity
Dynamical Systems as Models of the World
Practice, Technical Skills and Selected Topics
Algorithmic Information Dynamics and Reprogrammability
Applications to Behavioural, Evolutionary and Molecular Biology
certificate of completion if fee is paid
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