Course Description

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.

Course curriculum

  • 1
  • 2

    Unit 1: Introduction to Information

  • 3

    Unit 2: Shannon Information Theory

    • Lecture 4: Information and Shannon Information

    • Quiz 4

    • Lecture 5: Twenty Questions

    • Quiz 5

    • Lecture 6: Bits as Coin Flips

    • Quiz 6

  • 4

    Unit 3: Intro to Design & Search

    • 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

  • 5

    Unit 4: Making Design & Search Work

    • Lecture 11: Assisted Search

    • Quiz 11

    • Lecture 12: Evolutionary Search

    • Quiz 12

  • 6

    Unit 5: Further Aspects of Design & Search

    • 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

  • 7

    Unit 6: Conservation of Information

    • 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

  • 8

    Unit 7: Modeling Conservation of Information

    • 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

  • 9

    Unit 8: Limitations of Evolutionary Search

    • 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

  • 10

    Unit 9: Deconstruction of Avida

    • Lecture 34: AVIDA and the Law

    • Quiz 34

    • Lecture 35: AVIDA NAND Logic

    • Quiz 35

    • Lecture 36: AVIDA Evolution

    • Quiz 36

  • 11

    Unit 10: Measuring Meaning in Design

    • 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

Instructor(s)

Robert Marks

Director, Bradley Center for Natural & Artificial Intelligence

Robert J. Marks is Distinguished Professor of Electrical and Computer Engineering at Baylor University and serves as Director of the Walter Bradley Center for Natural and Artificial Intelligence at Discovery Institute. Marks is a Fellow of both the Institute of Electrical and Electronic Engineers (IEEE) and the Optical Society of America. He was Charter President of the IEEE Neural Networks Council and served as Editor-in-Chief of the IEEE Transactions on Neural Networks. He is coauthor of the books Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (MIT Press) and Introduction to Evolutionary Informatics (World Scientific). Dr. Marks holds a PhD in Electrical Engineering from Texas Tech University.