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Episode | Date |
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LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
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Jul 20, 2021 |
LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges
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May 21, 2021 |
LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
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Jan 05, 2021 |
LM101-083: Ch5: How to Use Calculus to Design Learning Machines
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Aug 29, 2020 |
LM101-082: Ch4: How to Analyze and Design Linear Machines
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Jul 23, 2020 |
LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
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Apr 09, 2020 |
LM101-080: Ch2: How to Represent Knowledge using Set Theory
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Feb 29, 2020 |
LM101-079: Ch1: How to View Learning as Risk Minimization
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Dec 24, 2019 |
LM101-078: Ch0: How to Become a Machine Learning Expert
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Oct 24, 2019 |
LM101-077: How to Choose the Best Model using BIC
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May 02, 2019 |
LM101-076: How to Choose the Best Model using AIC and GAIC
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Jan 23, 2019 |
LM101-075: Can computers think? A Mathematician's Response (remix)
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Dec 12, 2018 |
LM101-074: How to Represent Knowledge using Logical Rules (remix)
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Jun 30, 2018 |
LM101-073: How to Build a Machine that Learns to Play Checkers (remix)
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Apr 25, 2018 |
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)
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Mar 31, 2018 |
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
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Feb 23, 2018 |
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
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Jan 31, 2018 |
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
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Dec 16, 2017 |
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
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Sep 26, 2017 |
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
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Aug 21, 2017 |
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
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Jul 17, 2017 |
LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
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Jun 19, 2017 |
LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
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May 15, 2017 |
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
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Apr 20, 2017 |
LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine
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Mar 19, 2017 |
LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN)
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Feb 23, 2017 |
LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms
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Jan 23, 2017 |
LM101-059: How to Properly Introduce a Neural Network
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Dec 21, 2016 |
LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis
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Nov 23, 2016 |
LM101-057: How to Catch Spammers using Spectral Clustering
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Oct 18, 2016 |
LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
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Sep 20, 2016 |
LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun)
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Aug 16, 2016 |
LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)
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Jul 25, 2016 |
LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization)
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Jul 11, 2016 |
LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear
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Jun 13, 2016 |
LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning[Rerun]
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May 24, 2016 |
LM101-050: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
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May 04, 2016 |
LM101-049: How to Experiment with Lunar Lander Software
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Apr 22, 2016 |
LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun)
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Mar 29, 2016 |
LM101-047: How Build a Support Vector Machine to Classify Patterns (Rerun)
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Mar 14, 2016 |
LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)
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Feb 23, 2016 |
LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images
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Feb 08, 2016 |
LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?
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Jan 26, 2016 |
LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)
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Jan 12, 2016 |
LM101-042: What happened at the Monte Carlo Markov Chain (MCMC) Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference?
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Dec 29, 2015 |
LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?
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Dec 16, 2015 |
LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis
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Nov 24, 2015 |
LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun]
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Nov 09, 2015 |
LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets
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Oct 27, 2015 |
LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory
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Oct 12, 2015 |
LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks
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Sep 28, 2015 |
LM101-035: What is a Neural Network and What is a Hot Dog?
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Sep 15, 2015 |
LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun]
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Aug 25, 2015 |
LM101-033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
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Aug 10, 2015 |
LM101-032: How To Build a Support Vector Machine to Classify Patterns
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Jul 13, 2015 |
LM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN)
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Jun 21, 2015 |
LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)
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Jun 08, 2015 |
LM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling
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May 25, 2015 |
LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN]
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May 11, 2015 |
LM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN]
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Apr 28, 2015 |
LM101-026: How to Learn Statistical Regularities (Rerun)
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Apr 14, 2015 |
LM101-025: How to Build a Lunar Lander Autopilot Learning Machine
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Mar 24, 2015 |
LM101-024: How to Use Genetic Algorithms to Breed Learning Machines
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Mar 10, 2015 |
LM101-023: How to Build a Deep Learning Machine
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Feb 24, 2015 |
LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems
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Feb 10, 2015 |
LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)
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Jan 26, 2015 |
LM101-020: How to Use Nonlinear Machine Learning Software to Make Predictions
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Jan 12, 2015 |
LM101-019 (Rerun): How to Enhance Intelligence with a Robotic Body (Embodied Cognition)
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Dec 22, 2014 |
LM101-018: Can Computers Think? A Mathematician's Response (Rerun)
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Dec 12, 2014 |
LM101-017: How to Decide if a Machine is Artificially Intelligent (Rerun)
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Nov 24, 2014 |
LM101-016: How to Analyze and Design Learning Rules using Gradient Descent Methods
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Nov 11, 2014 |
LM101-015: How to Build a Machine that Can Learn Anything (The Perceptron)
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Oct 27, 2014 |
LM101-014: How to Build a Machine that Can Do Anything (Function Approximation)
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Oct 13, 2014 |
LM101-013: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)
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Sep 22, 2014 |
LM101-012: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)
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Sep 09, 2014 |
LM101-008: How to Represent Beliefs Using Probability Theory
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Sep 03, 2014 |
LM101-011: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)
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Aug 26, 2014 |
LM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation)
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Aug 12, 2014 |
LM101-009: How to Enhance Intelligence with a Robotic Body (Embodied Cognition)
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Jul 28, 2014 |
LM101-007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory
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Jun 23, 2014 |
LM101-006: How to Interpret Turing Test Results
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Jun 09, 2014 |
LM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test)
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May 27, 2014 |
LM101-004: Can computers think? A mathematician.s response
|
May 12, 2014 |
LM101-003: How to Represent Knowledge using Logical Rules
|
Apr 29, 2014 |
LM101-002: How to Build a Machine that Learns to Play Checkers
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Apr 29, 2014 |