EVOLUTIONARY INFORMATICS
TAUGHT BY ROBERT J. MARKS II
Evolutionary Informatics provides an introduction to information theory and its application to the limits of computing, including machine intelligence and evolutionary computing. Topics include introductory material on Shannon and Kolmogorov information theory, the absolute necessity of domain expertise in search and design, conservation of information, measuring the information infused in computer programs by the programmer, defining when the improbable becomes impossible, problems that computers will never solve, and how meaningful information can be measured. Hands-on experience is provided with access to interactive software. Note: It is helpful to have a basic understanding of probability to complete the course.
Your instructor for Evolutionary Informatics is Robert J. Marks, PhD, who is Director of Discovery Institute’s Bradley Center for Natural and Artificial Intelligence and who is a Distinguished Professor of Electrical & Computer Engineering at Baylor University. The course is based on his book coauthored with William Dembski and Winston Ewert, Introduction to Evolutionary Informatics (World Scientific). Purchase of the book for the course is encouraged but not required.
Each lesson within the course is comprised of a video and a corresponding quiz which you must pass with 75-100% before moving on (the pass percentage depends on the length of the quiz). There are 41 short video lectures in the course. (If you don't want to take the quizzes, you can move ahead to whatever lecture you want by using the left-hand course navigation.)
Important Information: Upon enrolling in this course, you will have 365 days to complete it. After 365 days, your course access will expire. Also, if you have trouble in creating a new user account, please turn off your ad or pop-up blockers. Sometimes those features interfere with the registration process.
Lecture 1: Introduction
FREE PREVIEWQuiz 1
FREE PREVIEWLecture 2: Science and Models
FREE PREVIEWQuiz 2
Lecture 3: What is Information?
Quiz 3
Lecture 4: Information and Shannon Information
Quiz 4
Lecture 5: Twenty Questions
Quiz 5
Lecture 6: Bits as Coin Flips
Quiz 6
Lecture 7: Iterative Design Principles
Quiz 7
Lecture 8: The Search for a Good Recipe Part I: The Fitness Landscape
Quiz 8
Lecture 9: The Search of a Good Recipe Part II: The Curse of Dimensionality
Quiz 9
Lecture 10: The Search for a Good Recipe Part III: Computer Search
Quiz 10
Lecture 11: Assisted Search
Quiz 11
Lecture 12: Evolutionary Search
Quiz 12
Lecture 13: Sources of Knowledge
Quiz 13
Lecture 14: Weasel Ware
Quiz 14
Lecture 15: Weasel Ware Screencast
Quiz 15
Lecture 16: Multi-Objective Design
Quiz 16
Lecture 17: Bernoulli's Principle of Insufficient Reason
Quiz 17
Lecture 18: Beating Bernoulli
Quiz 18
Lecture 19: Conservation of Information
Quiz 19
Lecture 20: Conservation of Information II
Quiz 20
Lecture 21: Possibility vs. Probability
Quiz 21
Lecture 22: Borel's Law and the Universal Probability Bound
Quiz 22
Lecture 23: Hamlet and Probability
Quiz 23
Lecture 24: Endogenous Information
Quiz 24
Lecture 25: Active Information I
Quiz 25
Lecture 26: Active Information II
Quiz 26
Lecture 27: Evolutionary Search
Quiz 27
Lecture 28: The Need for Noise
Quiz 28
Lecture 29: EV
Quiz 29
Lecture 30: EV-ware
Quiz 30
Lecture 31: Adaptation and Design
Quiz 31
Lecture 32: Tierra and Basener's Ceiling
Quiz 32
Lecture 33: Stairstep Information and Transitional Functional Viability
Quiz 33
Lecture 34: AVIDA and the Law
Quiz 34
Lecture 35: AVIDA NAND Logic
Quiz 35
Lecture 36: AVIDA Evolution
Quiz 36
Lecture 37: Kolmogorov Complexity
Quiz 37
Lecture 38: Meaning and Context
Quiz 38
Lecture 39: KCS Information and Context
Quiz 39
Lecture 40: Poker, Snowflakes, and Algorithmic Specified Complexity
Quiz 40
Lecture 41: Conclusion
Quiz 41