Learning Machines 101
En podcast av Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.
Kategorier:
85 Avsnitt
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LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
Publicerades: 2021-07-20 -
LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges
Publicerades: 2021-05-21 -
LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
Publicerades: 2021-01-05 -
LM101-083: Ch5: How to Use Calculus to Design Learning Machines
Publicerades: 2020-08-29 -
LM1010-082: Ch4: How to Analyze and Design Linear Machines
Publicerades: 2020-07-23 -
LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
Publicerades: 2020-04-09 -
LM101-080: Ch2: How to Represent Knowledge using Set Theory
Publicerades: 2020-02-29 -
LM101-079: Ch1: How to View Learning as Risk Minimization
Publicerades: 2019-12-24 -
LM101-078: Ch0: How to Become a Machine Learning Expert
Publicerades: 2019-10-24 -
LM101-077: How to Choose the Best Model using BIC
Publicerades: 2019-05-02 -
LM101-076: How to Choose the Best Model using AIC and GAIC
Publicerades: 2019-01-23 -
LM101-075: Can computers think? A Mathematician's Response (remix)
Publicerades: 2018-12-12 -
LM101-074: How to Represent Knowledge using Logical Rules (remix)
Publicerades: 2018-06-30 -
LM101-073: How to Build a Machine that Learns to Play Checkers (remix)
Publicerades: 2018-04-25 -
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)
Publicerades: 2018-03-31 -
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
Publicerades: 2018-02-23 -
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
Publicerades: 2018-01-31 -
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
Publicerades: 2017-12-16 -
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
Publicerades: 2017-09-26 -
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
Publicerades: 2017-08-21
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!
