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