Learning Machines 101

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

Categories:

Technology

85 Episodes

  1. LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)

    Published: 2/23/2016

  2. LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images

    Published: 2/8/2016

  3. LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?

    Published: 1/26/2016

  4. LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)

    Published: 1/12/2016

  5. LM101-042: What happened at the Monte Carlo Markov Chain (MCMC) Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference?

    Published: 12/29/2015

  6. LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?

    Published: 12/16/2015

  7. LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis

    Published: 11/24/2015

  8. LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun]

    Published: 11/9/2015

  9. LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets

    Published: 10/27/2015

  10. LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory

    Published: 10/12/2015

  11. LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks

    Published: 9/28/2015

  12. LM101-035: What is a Neural Network and What is a Hot Dog?

    Published: 9/15/2015

  13. LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun]

    Published: 8/25/2015

  14. LM101-033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]

    Published: 8/10/2015

  15. LM101-032: How To Build a Support Vector Machine to Classify Patterns

    Published: 7/13/2015

  16. LM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN)

    Published: 6/21/2015

  17. LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)

    Published: 6/8/2015

  18. LM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling

    Published: 5/25/2015

  19. LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN]

    Published: 5/11/2015

  20. LM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN]

    Published: 4/28/2015

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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!