This podcast is basically a remix of the first and second episodes of Learning Machines 101 and is intended to serve as the new introduction to the Learning Machines 101 podcast series. The search for common organizing principles which could support the foundations of machine learning and artificial intelligence is discussed and the concept of the Big Artificial Intelligence Magic Show is introduced. At the end of the podcast, the book After Digital: Computation as Done by Brains and Machines by Professor James A. Anderson is briefly reviewed. For more information, please visit: www.learningmachines101.com
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine
LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN)
LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms
LM101-059: How to Properly Introduce a Neural Network
LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis
LM101-057: How to Catch Spammers using Spectral Clustering
LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun)
LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)
LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization)
LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear
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)
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