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:
LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks
LM101-035: What is a Neural Network and What is a Hot Dog?
LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun]
LM101-033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
LM101-032: How To Build a Support Vector Machine to Classify Patterns
LM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN)
LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)
LM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling
LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN]
LM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN]
LM101-026: How to Learn Statistical Regularities (Rerun)
LM101-025: How to Build a Lunar Lander Autopilot Learning Machine
LM101-024: How to Use Genetic Algorithms to Breed Learning Machines
LM101-023: How to Build a Deep Learning Machine
LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems
