Learning Machines 101
Summary: 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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- Artist: Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.
- Copyright: Copyright (c) 2014-2019 by Richard M. Golden. All rights reserved.
Podcasts:
LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning[Rerun]
LM101-050: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
LM101-049: How to Experiment with Lunar Lander Software
LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun)
LM101-047: How Build a Support Vector Machine to Classify Patterns (Rerun)
LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)
LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images
LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?
LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)
LM101-042: What happened at the Monte Carlo Markov Chain (MCMC) Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference?
LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?
LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis
LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun]
LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets
LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory
