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: IntroductionFREE PREVIEW
Quiz 1FREE PREVIEW
Lecture 2: Science and ModelsFREE PREVIEW
Lecture 3: What is Information?
Lecture 4: Information and Shannon Information
Lecture 5: Twenty Questions
Lecture 6: Bits as Coin Flips
Lecture 7: Iterative Design Principles
Lecture 8: The Search for a Good Recipe Part I: The Fitness Landscape
Lecture 9: The Search of a Good Recipe Part II: The Curse of Dimensionality
Lecture 10: The Search for a Good Recipe Part III: Computer Search
Lecture 11: Assisted Search
Lecture 12: Evolutionary Search
Lecture 13: Sources of Knowledge
Lecture 14: Weasel Ware
Lecture 15: Weasel Ware Screencast
Lecture 16: Multi-Objective Design
Lecture 17: Bernoulli's Principle of Insufficient Reason
Lecture 18: Beating Bernoulli