Description
Learning Machines 101 is a podcast series hosted by Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E., dedicated to exploring the principles of artificial intelligence and machine learning. The podcast aims to answer fundamental questions about the smart machines that are increasingly prevalent in our daily lives, such as how they function, where they originate, and how they can be made more intelligent and human-like.
Each episode delves into specific aspects of AI and machine learning, often referencing chapters from Golden's book, "Statistical Machine Learning: A unified framework." Topics covered range from the mathematical underpinnings of machine learning, such as probability theory and measure theory, to practical applications and algorithm design. For instance, episodes discuss how to learn probabilities of infinite outcomes, guarantee convergence of batch learning algorithms, analyze dynamical systems, and utilize calculus for designing learning machines.
The podcast series is designed for a broad audience, from beginners interested in understanding the basics of AI to advanced practitioners seeking deeper insights. It breaks down complex concepts into accessible explanations, making it a valuable resource for students, researchers, and anyone curious about the technology shaping our world. The host's background, with extensive citations in machine learning dating back to the 1980s, lends significant credibility to the content presented.
Learning Machines 101 provides a structured approach to understanding machine learning, often linking theoretical concepts to practical implementations and future advancements. The podcast encourages listeners to think critically about AI's role and potential, fostering a deeper appreciation for the science behind intelligent systems. The series is updated bimonthly, ensuring a consistent flow of new information and discussions on the evolving field of artificial intelligence.
What You'll Get
Explores AI and machine learning principles
Discusses how smart machines work
Examines the origins of AI technologies
Covers methods for making AI more human-like
Features episodes based on statistical machine learning concepts
Includes discussions on probability theory and measure theory
Explains convergence guarantees for learning algorithms
Analyzes machine learning algorithms as dynamical systems
Utilizes calculus for learning machine design
Covers linear algebra applications in machine learning
Defines machine learning algorithms formally
Explains knowledge representation using set theory
Views learning as risk minimization
How to Follow Learning Machines 101
Find the podcast on Apple Podcasts
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Listen to episodes on AI and machine learning
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Who Learning Machines 101 Is Best For
- AI Education
- Machine Learning Theory
- Algorithm Design
- AI Applications
- Continuing Education
- Research Insights
- Conceptual Understanding
