Hello and welcome. This course is a conceptual introduction to the new field of algorithmic information dynamics,

conceptual means that sometimes we won't get into the details

but we will give you the pointers to the proper literature, mostly papers on which you can dig as deep as you may want.

What we wanted to offer is a course that you weren't able to find anywhere else,

no content of this course has ever been published in a textbook, and for some of the preliminaries there is hardly any other online course,

such as for computability or algorithmic complexity.

So we will introduce this new exciting field of algorithmic information dynamics

and we will go through established but equally

exciting areas of science that are needed to understand the more advanced topics and more recent developments.

The first module will be mostly a motivational introduction to the many concepts involved in the study of algorithmic information

dynamics, in particular the approaches taken in the past to deal with the challenge of

causality or causation on which we have built upon.

The challenge of causality or causation is about developing methods to find or access the true causes for things to happen.

These areas that we will briefly explore include philosophical and mathematical logic,

traditional statistics and classical probability theory dynamical systems,

and also what is today called machine learning.

We will then briefly introduce you to graphs and networks, both from the mathematical perspective and from

applications, in particular the way in which they serve to represent

interactions in biology.

Networks will be a fundamental object of study throughout the course.

In another module, we will walk you through another fundamental area, that of dynamical systems,

one of the main concepts needed to understand algorithmic information dynamics, where we can study systems evolving over time.

Then we will have the difficult task to give a very general overview of information theory, computability theory, and algorithmic

complexity, the three most important topics other than dynamical systems to understand the field of algorithmic information dynamics.

Finally, towards the end,

we will introduce the main concepts of algorithmic information dynamics and the fundamental concept of reprogrammability.

Algorithmic Information Dynamics puts everything together,

networks, evolving systems, perturbation analysis,

complexity, computability and information theory to help tackle the question at the heart of this course,

that is the question to find mechanistic causes for natural and artificial phenomena.

This is the reason we have to go through a good deal of theory and concepts before getting into the main subject

so we will have to cover a lot before getting into the actual purpose of the course.

There will be a practice module led by Alyssa who will be covering some special topics based on questions and comments from students

made in the forum over the first weeks.

We will rank those questions and Alyssa will be preparing lectures around those topics chosen by students. In the last module

we will explore the various applications of algorithmic information

dynamics in particular to behavioural,

evolutionary and molecular biology, to static and evolving genetic networks and

in the new area of algorithmic machine learning with pointers towards future research.

In the last two modules

you will find methods that will help you frame your own questions and give you the means to find answers using your own data and

our tools.

We will be happy to follow the applications you come up with, even help you or collaborate with you in some of your projects

or ours.

Those people willing to write a manuscript will be evaluated, selected from the best applications and

invited to submit to the journal of Complex Systems to disseminate their results.

This is our first online course and something like an experiment for us for which we would very much appreciate your feedback.

The plan is to keep updating the course in the future, so thanks again for watching.