Learning Machines 101
Science
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!
Location:
Allen, TX
Description:
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!
Language:
English
Email:
rmgconsult@aol.com
LM101-082: Ch4: How to Analyze and Design Linear Machines
Duration:00:29:04
LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
Duration:00:48:31
LM101-080: Ch2: How to Represent Knowledge using Set Theory
Duration:00:41:31
LM101-079: Ch1: How to View Learning as Risk Minimization
Duration:00:34:31
LM101-078: Ch0: How to Become a Machine Learning Expert
Duration:00:39:17
LM101-077: How to Choose the Best Model using BIC
Duration:00:24:14
LM101-076: How to Choose the Best Model using AIC and GAIC
Duration:00:28:16
LM101-075: Can computers think? A Mathematician's Response (remix)
Duration:00:36:25
LM101-074: How to Represent Knowledge using Logical Rules (remix)
Duration:00:19:21
LM101-073: How to Build a Machine that Learns to Play Checkers (remix)
Duration:00:24:57
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)
Duration:00:22:06
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
Duration:00:31:39
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
Duration:00:32:03
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
Duration:00:23:19
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
Duration:00:21:48
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
Duration:00:25:39
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
Duration:00:33:59
LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
Duration:00:29:59
LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
Duration:00:28:03
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
Duration:00:22:03