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Learning Machines 101

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 which will be addressed in the podcast series Learning Machines 101.

info_outline LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes 07/20/2021
info_outline LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges 05/21/2021
info_outline LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems 01/05/2021
info_outline LM101-083: Ch5: How to Use Calculus to Design Learning Machines 08/29/2020
info_outline LM101-082: Ch4: How to Analyze and Design Linear Machines 07/23/2020
info_outline LM101-081: Ch3: How to Define Machine Learning (or at Least Try) 04/09/2020
info_outline LM101-080: Ch2: How to Represent Knowledge using Set Theory 02/29/2020
info_outline LM101-079: Ch1: How to View Learning as Risk Minimization 12/24/2019
info_outline LM101-078: Ch0: How to Become a Machine Learning Expert 10/24/2019
info_outline LM101-077: How to Choose the Best Model using BIC 05/02/2019
info_outline LM101-076: How to Choose the Best Model using AIC and GAIC 01/23/2019
info_outline LM101-075: Can computers think? A Mathematician's Response (remix) 12/12/2018
info_outline LM101-074: How to Represent Knowledge using Logical Rules (remix) 06/30/2018
info_outline LM101-073: How to Build a Machine that Learns to Play Checkers (remix) 04/25/2018
info_outline LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002) 03/31/2018
info_outline LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets 02/23/2018
info_outline LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding 01/31/2018
info_outline LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference? 12/16/2017
info_outline LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms 09/26/2017
info_outline LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun) 08/14/2017
info_outline LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun) 07/17/2017
info_outline LM101-065: How to Design Gradient Descent Learning Machines (Rerun) 06/02/2017
info_outline LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun) 05/15/2017
info_outline LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine 04/20/2017
info_outline LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine 03/19/2017
info_outline LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN) 02/23/2017
info_outline LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms 01/23/2017
info_outline LM101-059: How to Properly Introduce a Neural Network 12/21/2016
info_outline LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis 11/23/2016
info_outline LM101-057: How to Catch Spammers using Spectral Clustering 10/18/2016
 
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