All Episodes

LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes

LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges

LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems

LM101-083: Ch5: How to Use Calculus to Design Learning Machines

LM101-082: Ch4: How to Analyze and Design Linear Machines

LM101-081: Ch3: How to Define Machine Learning (or at Least Try)

LM101-080: Ch2: How to Represent Knowledge using Set Theory

LM101-079: Ch1: How to View Learning as Risk Minimization

LM101-078: Ch0: How to Become a Machine Learning Expert

LM101-077: How to Choose the Best Model using BIC

LM101-076: How to Choose the Best Model using AIC and GAIC

LM101-075: Can computers think? A Mathematician's Response (remix)

LM101-074: How to Represent Knowledge using Logical Rules (remix)

LM101-073: How to Build a Machine that Learns to Play Checkers (remix)

LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)

LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets

LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding

LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?

LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms

LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)

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)

LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)

LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images

LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?

LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)

LM101-042: What happened at the Monte Carlo Markov Chain (MCMC) Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference?

LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?

LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis

LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun]

LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets

LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory

LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks

LM101-035: What is a Neural Network and What is a Hot Dog?

LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun]

LM101-033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]

LM101-032: How To Build a Support Vector Machine to Classify Patterns

LM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN)

LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)

LM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling

LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN]

LM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN]

LM101-026: How to Learn Statistical Regularities (Rerun)

LM101-025: How to Build a Lunar Lander Autopilot Learning Machine

LM101-024: How to Use Genetic Algorithms to Breed Learning Machines

LM101-023: How to Build a Deep Learning Machine

LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems

LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)

LM101-020: How to Use Nonlinear Machine Learning Software to Make Predictions

LM101-019 (Rerun): How to Enhance Intelligence with a Robotic Body (Embodied Cognition)

LM101-018: Can Computers Think? A Mathematician's Response (Rerun)

LM101-017: How to Decide if a Machine is Artificially Intelligent (Rerun)

LM101-016: How to Analyze and Design Learning Rules using Gradient Descent Methods

LM101-015: How to Build a Machine that Can Learn Anything (The Perceptron)

LM101-014: How to Build a Machine that Can Do Anything (Function Approximation)

LM101-013: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)

LM101-012: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)

LM101-008: How to Represent Beliefs Using Probability Theory

LM101-011: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)

LM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation)

LM101-009: How to Enhance Intelligence with a Robotic Body (Embodied Cognition)

LM101-007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory

LM101-006: How to Interpret Turing Test Results

LM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test)

LM101-004: Can computers think? A mathematician.s response

LM101-003: How to Represent Knowledge using Logical Rules

LM101-002: How to Build a Machine that Learns to Play Checkers