-
-
Course Introduction
-
The Challenge of Causality
-
What is a Mechanistic Model?
-
Logic, Reasoning, and Simplicity
-
Probability and Statistics
-
Perturbation Analysis and Machine Learning
-
Computation in Causality
-
Modeling from Observation
-
Causality as Computation
-
Inference and Complexity
-
The Future of Causality
-
-
-
Basic Definitions
-
Subgraphs and Graph Representations
-
Isomorphic Graphs
-
Connectivity and Trees
-
Properties of Graphs
-
Network Models
-
Biological Networks
-
Unit 2 Exercises
-
-
-
A Probabilistic Law
-
Classical Information
-
Shannon Entropy and Meaning
-
Joint, Conditional, Mutual Information, and a Case Study
-
Entropy Rate and Multidimensional Objects
-
Redundancy, Noise, and Biological Information
-
Turing Machines
-
The Halting Problem and Turing Universality
-
Busy Beaver Functions and the Decidability Frontier
-
Enumeration and Systems Behavior
-
Computability and Predictability
-
Demo 1: Cellular Automata
-
Demo 2: Constructing Networks
-
Demo 3: Networks from Real Data
-
-
-
Intuitive Randomness
-
Sources of Randomness
-
Pseudo-Randomness
-
Algorithmic Complexity and Compressibility
-
The Invariance Theorem
-
Convergence of Definitions
-
Chaitin's Omega Number
-
Epistemological Aspects of Infinite Wisdom
-
The Chaitin-Leibniz Medal
-
Algorithmic Probability and the Universal Distribution
-
The Infinite Programming Monkey
-
The Algorithmic Coding Theorem
-
Logical Depth: A Measure of Sophistication
-
Unit 4 Quiz
-
-
-
What is a Dynamical System?
-
Dynamical Systems Terminology
-
Dynamical Systems Analysis
-
2D Dynamical Systems
-
The Lorenz System
-
Discrete Dynamical Systems
-
Kauffman Model
-
Unit 5 Quiz
-
-
-
Limitations of Shannon Entropy and Computable Measures
-
Computing the Uncomputable: The Coding Theorem Method
-
Shannon Entropy and Algorithmic Complexity Joining Forces: The Block Decomposition Method (BDM)
-
Summary: The Block Decomposition Method (BDM)
-
Graph and Tensor Algorithmic Complexity
-
Summary: Graph Complexity
-
Algorithmic Information Dynamics
-
Moving Objects Towards & Away From Randomness
-
Reconstructing Dynamical Systems
-
System's Reprogrammability
-
Summary: A Reprogrammable World
-
-
-
Does Nature Compute?
-
Natural Phenomena as Computation
-
How Complexity Characterizes Life
-
Algorithmic Dynamics of Graph Spectra and Network Reconstruction
-
Applications to Molecular Biology and Genetic Networks
-
Cell Dynamics and Attractor Landscapes
-
Integer and Behavioural Sequences, and Algorithmic Cognition
-
Summary: Algorithmic Cognition
-
Evolutionary Biology and Genetic Algorithms
-
Summary: Steering and Reprogramming Life
-
Applications of Logical Depth to Image Classification
-
Algorithmic Machine Learning
-
The Future of Algorithmic Information Dynamics
-
-
-
Final Exam
-
5.7 Kauffman Model » 5.7 Kauffman Model Download Video Subtitles & Transcripts
Download the subtitles for all the Unit 5 videos here: https://www.complexityexplorer.org/system//courses/media/files/000/001/609/original/text_5.dynamical_system.docx?1535306076