Learning Machines 101: A Gentle Introduction to Artificial Intelligence and Machine Learning

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About Learning Machines 101

Smart machines employing artificial intelligence and machine learning are prevalent in everyday life.

For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, trade stocks on the stock market, repel computer viruses, make purchasing suggestions on Amazon, deliver information upon request from Google, and automatically pilot vehicles on the groundair, and outer space.

Such machines can even mimic biological human learning and biological human information processing!

These are just a few examples of how smart machines and artificial intelligence impact our everyday lives.

In this podcast series, we examine such questions as:

  • How do these devices work?
  • Where do they come from?
  • How can we make them even smarter?
  • And how can we make them even more human-like?

Learning Machines 101 is committed to providing an accessible introduction to the complex and fascinating world of Artificial Intelligence which now has an impact on everyday life throughout the world!

Who is the Intended Audience?

The intended audience for this podcast series is the general public and the intended objective of this podcast series is to help popularize and demystify the field of Artificial Intelligence by explaining fundamental concepts in an entertaining manner. However, many advanced topics in artificial intelligence and machine learning will be discussed at a “high-level” so students, scientists, and engineers working in the machine learning area may find this podcast series beneficial for identifying relevant “entry points” into advanced statistical machine learning topics. Relevant references to advanced readings are provided (when applicable) in the show notes for each episode.


About Dr. Richard M. Golden

Dr. Richard M. Golden obtained the Bachelor of Science degree in Electrical Engineering with a specialization in Communication Systems from the University of California of San Diego in 1982. He received the Master of Science degree in Electrical Engineering with a specialization in Statistical Pattern Recognition from Brown University in 1986, and Ph.D. in Experimental Psychology with a specialization in Cognitive Science from Brown University in 1987. Dr. Golden was an Andrew Mellon Fellow at the University of Pittsburgh from 1987-1988, and an NIH Post-doctoral Scholar at Stanford University from 1988-1990.

Over the past three decades, Dr. Richard M. Golden has published scientific articles and given presentations on the following topics.

  • When does a learning machine really understand its environment?
  • How will a learning machine behave in a new situation?
  • If a learning machine has a misconception about its world, what can we say about the learning machine’s behavior in new situations?
  • Which learning machine is most appropriate for a particular environment?

Dr. Richard M. Golden has also collaborated with other scientists and engineers on a variety of problems in the area of artificial intelligence including:  predictions of medical conditions and epidemiological data analysis, automated document clustering, automated detection of software programming errors, automated smart antenna signal processing applications, automated circuit design, and automated aircraft landing.

Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering and part-time  Statistical Machine Learning Consultant. In addition, Dr. Richard M. Golden is Secretary Treasurer of the Society for Mathematical Psychology which is an international society concerned with the development of formal mathematical models of human and animal behavior.

Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of the International Journal of Applied Intelligence from 2001-2004, and a member of the Editorial Board of the journal Neural Networks from 1995-2006.

Dr. Richard M. Golden is the author of the book Mathematical Methods for Neural Network Analysis and Design, has published over 75 articles in scientific journals in the areas of theoretical statistical machine learning, and  statistical machine learning applications,  has given over 78 presentations at international scientific conferences, and is the co-inventor of three U.S. patents.