Learning Machines 101
By Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.
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Category: Technology
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Episodes: 85
Description
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 that will be addressed in this podcast series!
| Episode | Date |
|---|---|
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LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
|
Jul 20, 2021 |
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LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges
|
May 21, 2021 |
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LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
|
Jan 05, 2021 |
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LM101-083: Ch5: How to Use Calculus to Design Learning Machines
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Aug 29, 2020 |
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LM101-082: Ch4: How to Analyze and Design Linear Machines
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Jul 23, 2020 |
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LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
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Apr 09, 2020 |
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LM101-080: Ch2: How to Represent Knowledge using Set Theory
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Feb 29, 2020 |
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LM101-079: Ch1: How to View Learning as Risk Minimization
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Dec 24, 2019 |
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LM101-078: Ch0: How to Become a Machine Learning Expert
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Oct 24, 2019 |
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LM101-077: How to Choose the Best Model using BIC
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May 02, 2019 |
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LM101-076: How to Choose the Best Model using AIC and GAIC
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Jan 23, 2019 |
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LM101-075: Can computers think? A Mathematician's Response (remix)
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Dec 12, 2018 |
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LM101-074: How to Represent Knowledge using Logical Rules (remix)
|
Jun 30, 2018 |
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LM101-073: How to Build a Machine that Learns to Play Checkers (remix)
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Apr 25, 2018 |
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LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)
|
Mar 31, 2018 |
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LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
|
Feb 23, 2018 |
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LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
|
Jan 31, 2018 |
|
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
|
Dec 16, 2017 |
|
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
|
Sep 26, 2017 |
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LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
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Aug 21, 2017 |
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LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
|
Jul 17, 2017 |
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LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
|
Jun 19, 2017 |
|
LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
|
May 15, 2017 |
|
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
|
Apr 20, 2017 |
|
LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine
|
Mar 19, 2017 |
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LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN)
|
Feb 23, 2017 |
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LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms
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Jan 23, 2017 |
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LM101-059: How to Properly Introduce a Neural Network
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Dec 21, 2016 |
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LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis
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Nov 23, 2016 |
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LM101-057: How to Catch Spammers using Spectral Clustering
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Oct 18, 2016 |
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LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
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Sep 20, 2016 |
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LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun)
|
Aug 16, 2016 |
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LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)
|
Jul 25, 2016 |
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LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization)
|
Jul 11, 2016 |
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LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear
|
Jun 13, 2016 |
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LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning[Rerun]
|
May 24, 2016 |
|
LM101-050: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
|
May 04, 2016 |
|
LM101-049: How to Experiment with Lunar Lander Software
|
Apr 22, 2016 |
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LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun)
|
Mar 29, 2016 |
|
LM101-047: How Build a Support Vector Machine to Classify Patterns (Rerun)
|
Mar 14, 2016 |
|
LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)
|
Feb 23, 2016 |
|
LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images
|
Feb 08, 2016 |
|
LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?
|
Jan 26, 2016 |
|
LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)
|
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?
|
Dec 29, 2015 |
|
LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?
|
Dec 16, 2015 |
|
LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis
|
Nov 24, 2015 |
|
LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun]
|
Nov 09, 2015 |
|
LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets
|
Oct 27, 2015 |
|
LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory
|
Oct 12, 2015 |
|
LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks
|
Sep 28, 2015 |
|
LM101-035: What is a Neural Network and What is a Hot Dog?
|
Sep 15, 2015 |
|
LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun]
|
Aug 25, 2015 |
|
LM101-033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
|
Aug 10, 2015 |
|
LM101-032: How To Build a Support Vector Machine to Classify Patterns
|
Jul 13, 2015 |
|
LM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN)
|
Jun 21, 2015 |
|
LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)
|
Jun 08, 2015 |
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LM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling
|
May 25, 2015 |
|
LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN]
|
May 11, 2015 |
|
LM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN]
|
Apr 28, 2015 |
|
LM101-026: How to Learn Statistical Regularities (Rerun)
|
Apr 14, 2015 |
|
LM101-025: How to Build a Lunar Lander Autopilot Learning Machine
|
Mar 24, 2015 |
|
LM101-024: How to Use Genetic Algorithms to Breed Learning Machines
|
Mar 10, 2015 |
|
LM101-023: How to Build a Deep Learning Machine
|
Feb 24, 2015 |
|
LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems
|
Feb 10, 2015 |
|
LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)
|
Jan 26, 2015 |
|
LM101-020: How to Use Nonlinear Machine Learning Software to Make Predictions
|
Jan 12, 2015 |
|
LM101-019 (Rerun): How to Enhance Intelligence with a Robotic Body (Embodied Cognition)
|
Dec 22, 2014 |
|
LM101-018: Can Computers Think? A Mathematician's Response (Rerun)
|
Dec 12, 2014 |
|
LM101-017: How to Decide if a Machine is Artificially Intelligent (Rerun)
|
Nov 24, 2014 |
|
LM101-016: How to Analyze and Design Learning Rules using Gradient Descent Methods
|
Nov 11, 2014 |
|
LM101-015: How to Build a Machine that Can Learn Anything (The Perceptron)
|
Oct 27, 2014 |
|
LM101-014: How to Build a Machine that Can Do Anything (Function Approximation)
|
Oct 13, 2014 |
|
LM101-013: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)
|
Sep 22, 2014 |
|
LM101-012: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)
|
Sep 09, 2014 |
|
LM101-008: How to Represent Beliefs Using Probability Theory
|
Sep 03, 2014 |
|
LM101-011: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)
|
Aug 26, 2014 |
|
LM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation)
|
Aug 12, 2014 |
|
LM101-009: How to Enhance Intelligence with a Robotic Body (Embodied Cognition)
|
Jul 28, 2014 |
|
LM101-007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory
|
Jun 23, 2014 |
|
LM101-006: How to Interpret Turing Test Results
|
Jun 09, 2014 |
|
LM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test)
|
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
|
Apr 29, 2014 |