Learning Machines 101

A podcast by Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.

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Technology

85 Episodes

  1. LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes

    Published: 20/07/2021

  2. LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges

    Published: 21/05/2021

  3. LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems

    Published: 05/01/2021

  4. LM101-083: Ch5: How to Use Calculus to Design Learning Machines

    Published: 29/08/2020

  5. LM1010-082: Ch4: How to Analyze and Design Linear Machines

    Published: 23/07/2020

  6. LM101-081: Ch3: How to Define Machine Learning (or at Least Try)

    Published: 09/04/2020

  7. LM101-080: Ch2: How to Represent Knowledge using Set Theory

    Published: 29/02/2020

  8. LM101-079: Ch1: How to View Learning as Risk Minimization

    Published: 24/12/2019

  9. LM101-078: Ch0: How to Become a Machine Learning Expert

    Published: 24/10/2019

  10. LM101-077: How to Choose the Best Model using BIC

    Published: 02/05/2019

  11. LM101-076: How to Choose the Best Model using AIC and GAIC

    Published: 23/01/2019

  12. LM101-075: Can computers think? A Mathematician's Response (remix)

    Published: 12/12/2018

  13. LM101-074: How to Represent Knowledge using Logical Rules (remix)

    Published: 30/06/2018

  14. LM101-073: How to Build a Machine that Learns to Play Checkers (remix)

    Published: 25/04/2018

  15. LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)

    Published: 31/03/2018

  16. LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets

    Published: 23/02/2018

  17. LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding

    Published: 31/01/2018

  18. LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?

    Published: 16/12/2017

  19. LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms

    Published: 26/09/2017

  20. LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)

    Published: 21/08/2017

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Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life. For example, artificially intelligent systems recognize our voices, sort our pictures, make purchasing suggestions, and can automatically fly planes and drive cars. In this podcast series, we examine such questions such as: How do these devices work? Where do they come from? And how can we make them even smarter and more human-like? These are the questions that will be addressed in this podcast series!
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