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Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning

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Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning

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Learning Machines 101 is committed to providing an accessible introduction to the complex and fascinating world of Artificial Intelligence which now has an impact on everyday life throughout the world! The intended audience for this podcast series is the general public and the intended objective of this podcast series is to help popularize and de-mystify the field of Artificial Intelligence by explaining fundamental concepts in an entertaining manner. However, many advanced topics in artificial intelligence and machine learning will be discussed at a “high-level” so students, scientists, and engineers working in the machine learning area will find this podcast series beneficial for identifying relevant “entry points” into advanced statistical machine learning topics.

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locale en_US
type website
title Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning
description Learning Machines 101 is committed to providing an accessible introduction to the complex and fascinating world of Artificial Intelligence which now has an impact on everyday life throughout the world! The intended audience for this podcast series is the general public and the intended objective of this podcast series is to help popularize and de-mystify the field of Artificial Intelligence by explaining fundamental concepts in an entertaining manner. However, many advanced topics in artificial intelligence and machine learning will be discussed at a “high-level” so students, scientists, and engineers working in the machine learning area will find this podcast series beneficial for identifying relevant “entry points” into advanced statistical machine learning topics.
url https://www.learningmachines101.com/
site_name Learning Machines 101
image https://www.learningmachines101.com/wp-content/uploads/2021/07/Episode86graphicWideScreen.jpg
image:secure_url https://www.learningmachines101.com/wp-content/uploads/2021/07/Episode86graphicWideScreen.jpg
image:width 959
image:height 540

Niveaux de titre

H1 H2 H3 H4 H5 H6
1 50 0 0 0 0
  • [H1] Learning Machines 101
  • [H2] LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
  • [H2] LM101-085: Ch7: How to Guarantee your Batch Learning Algorithm Converges
  • [H2] LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
  • [H2] LM101-083: Ch5: How to Use Calculus to Design Learning Machines
  • [H2] LM101-082: Ch4: How to Analyze and Design Linear Machines
  • [H2] LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
  • [H2] LM101-080: Ch2: How to Represent Knowledge using Set Theory
  • [H2] LM101-079: Ch1: How to View Learning as Risk Minimization
  • [H2] LM101-078: Ch0: How to Become a Machine Learning Expert
  • [H2] LM101-077: How to Choose the Best Model using BIC
  • [H2] LM101-076: How To Choose the Best Model using AIC or GAIC
  • [H2] LM101-075: Can computers think? A Mathematician’s Response using a Turing Machine Argument (remix)
  • [H2] LM101-074: How to Represent Knowledge using Logical Rules (remix)
  • [H2] LM101-073: How to Build a Machine that Learns Checkers (remix)
  • [H2] LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (LM101-001+LM101-002 remix)
  • [H2] LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
  • [H2] LM101-070: How to Identify Facial Emotion Expressions Using Stochastic Neighborhood Embedding
  • [H2] LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
  • [H2] LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
  • [H2] LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
  • [H2] LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
  • [H2] LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
  • [H2] LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
  • [H2] LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
  • [H2] LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine
  • [H2] LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN)
  • [H2] LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms
  • [H2] LM101-059: How to Properly Introduce a Neural Network
  • [H2] LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis
  • [H2] LM101-057: How to Catch Spammers using Spectral Clustering
  • [H2] LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
  • [H2] LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun)
  • [H2] LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)
  • [H2] LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization)
  • [H2] LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear
  • [H2] LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning [Rerun]
  • [H2] LM101-050: How to Use Linear Regression Software to Make Predictions (RERUN)
  • [H2] LM101-049: How to Experiment with Lunar Lander Software
  • [H2] LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun)
  • [H2] LM101-047: How to Build a Support Vector Machine to Classify Patterns (Rerun)
  • [H2] LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)
  • [H2] LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images
  • [H2] LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?
  • [H2] LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun)
  • [H2] LM101-042: What happened at the Monte Carlo Markov Chain Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference?
  • [H2] LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?
  • [H2] LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis
  • [H2] LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)[Rerun]
  • [H2] LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets (Educational Technology)
  • [H2] LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory

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Learning Machines 101 Interne Passing Juice
Skip to content Interne Passing Juice
Home Interne Passing Juice
Join the Community! Interne Passing Juice
About Learning Machines 101 Interne Passing Juice
About Dr. Golden Interne Passing Juice
Episode Archive Interne Passing Juice
2020 Episodes Interne Passing Juice
2019 Episodes Interne Passing Juice
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Book Stuff! Interne Passing Juice
Dr. Goldens New Book! Externe Passing Juice
Book Review Archive Interne Passing Juice
Software Interne Passing Juice
Linear Machine Interne Passing Juice
Nonlinear Machine (RBF) Interne Passing Juice
Lunar Lander Software Interne Passing Juice
Licenses Interne Passing Juice
FAQ Interne Passing Juice
Contact Us Interne Passing Juice
LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes Interne Passing Juice
LM101-085: Ch7: How to Guarantee your Batch Learning Algorithm Converges Interne Passing Juice
LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems Interne Passing Juice
LM101-083: Ch5: How to Use Calculus to Design Learning Machines Interne Passing Juice
LM101-082: Ch4: How to Analyze and Design Linear Machines Interne Passing Juice
https://traffic.libsyn.com/secure/learningmachines101/LM101-086.mp3 Externe Passing Juice
Embed Interne noFollow
tweet Externe Passing Juice
BOOK Interne Passing Juice
Probabilistic Inference Interne Passing Juice
SMLBOOK Interne Passing Juice
Topic Interne Passing Juice
absolutely continuous density Interne Passing Juice
Banach-Tarski Interne Passing Juice
mixed random vector Interne Passing Juice
https://traffic.libsyn.com/secure/learningmachines101/LM101-085.mp3 Externe Passing Juice
Deep Learning Interne Passing Juice
Gradient Descent Learning Interne Passing Juice
batch learning Interne Passing Juice
descent direction Interne Passing Juice
gradient descent Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-084.mp3 Externe Passing Juice
book Interne Passing Juice
convergence Interne Passing Juice
dynamical systems Interne Passing Juice
matrix calculus Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-082.mp3 Externe Passing Juice
Machine Learning Interne Passing Juice
linear algebra Interne Passing Juice
matrix multiplication Interne Passing Juice
SVD Interne Passing Juice
LM101-081: Ch3: How to Define Machine Learning (or at Least Try) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-081.mp3 Externe Passing Juice
Book Review Interne Passing Juice
complete relation Interne Passing Juice
LM101-080: Ch2: How to Represent Knowledge using Set Theory Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-080.mp3 Externe Passing Juice
Features Interne Passing Juice
Rule-based Inference Interne Passing Juice
logic Interne Passing Juice
logical rules Interne Passing Juice
LM101-079: Ch1: How to View Learning as Risk Minimization Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/lm101-079.mp3 Externe Passing Juice
empirical risk Interne Passing Juice
reinforcement learning Interne Passing Juice
LM101-078: Ch0: How to Become a Machine Learning Expert Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-078.mp3 Externe Passing Juice
machine learning books Interne Passing Juice
machine learning mathematics Interne Passing Juice
machine learning software Interne Passing Juice
LM101-077: How to Choose the Best Model using BIC Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-077.mp3 Externe Passing Juice
Generalization Performance Interne Passing Juice
Model Selection Interne Passing Juice
Bayesian Information Criterion Interne Passing Juice
BIC Interne Passing Juice
Marginal Likelihood Interne Passing Juice
LM101-076: How To Choose the Best Model using AIC or GAIC Interne Passing Juice
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AIC Interne Passing Juice
Akaike Information Criterion Interne Passing Juice
cross-validation Interne Passing Juice
LM101-075: Can computers think? A Mathematician’s Response using a Turing Machine Argument (remix) Interne Passing Juice
LM101-074: How to Represent Knowledge using Logical Rules (remix) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-074.mp3 Externe Passing Juice
feature vector Interne Passing Juice
features Interne Passing Juice
LM101-073: How to Build a Machine that Learns Checkers (remix) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-073.mp3 Externe Passing Juice
Reinforcement Learning Interne Passing Juice
artificial intelligence Interne Passing Juice
Artificial Neural Networks Interne Passing Juice
Evaluation Function Interne Passing Juice
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (LM101-001+LM101-002 remix) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-072.mp3 Externe Passing Juice
learning machines 101 Interne Passing Juice
machine learning Interne Passing Juice
Neural Networks Interne Passing Juice
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-071.mp3 Externe Passing Juice
Constraint Satisfaction Interne Passing Juice
Monte Carlo Markov Chain Interne Passing Juice
common-sense knowledge Interne Passing Juice
CYC Interne Passing Juice
CYCL Interne Passing Juice
LM101-070: How to Identify Facial Emotion Expressions Using Stochastic Neighborhood Embedding Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-070.mp3 Externe Passing Juice
Clustering Algorithms Interne Passing Juice
Unsupervised Learning Interne Passing Juice
clustering Interne Passing Juice
Emotions Interne Passing Juice
Face Recognition Interne Passing Juice
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference? Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-069.mp3 Externe Passing Juice
curricula Interne Passing Juice
neural information processing systems Interne Passing Juice
NIPS 2017 Interne Passing Juice
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-068.mp3 Externe Passing Juice
Backtracking Linesearch Interne Passing Juice
Convergence Theorem Interne Passing Juice
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun) Interne Passing Juice
Boltzmann Machine Interne Passing Juice
Constraint Satisfaction Interne Passing Juice
Dreams Interne Passing Juice
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-066.mp3 Externe Passing Juice
Gibbs Sampler Interne Passing Juice
LM101-065: How to Design Gradient Descent Learning Machines (Rerun) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/lm101-065.mp3 Externe Passing Juice
line search Interne Passing Juice
LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-064.mp3 Externe Passing Juice
Genetic Algorithms Interne Passing Juice
Darwin Natural Selection Interne Passing Juice
Evolution Interne Passing Juice
genetic algorithm Interne Passing Juice
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-063.mp3 Externe Passing Juice
Expectation Maximization Interne Passing Juice
Monte Carlo Expectation Maximization Interne Passing Juice
policy gradient Interne Passing Juice
LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-062.mp3 Externe Passing Juice
Deep Reinforcement Learning Interne Passing Juice
Game playing Interne Passing Juice
Q learning Interne Passing Juice
LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-061.mp3 Externe Passing Juice
Deep Learning Interne Passing Juice
off-policy Interne Passing Juice
on-policy Interne Passing Juice
LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms Interne Passing Juice
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Anodot Interne Passing Juice
Anomaly Detection Interne Passing Juice
Berlin Buzzwords Interne Passing Juice
LM101-059: How to Properly Introduce a Neural Network Interne Passing Juice
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Biological Neural Networks Interne Passing Juice
Software Interne Passing Juice
biological neural networks Interne Passing Juice
Computational Neuroscience Interne Passing Juice
Convolutional Neural Networks Interne Passing Juice
LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-058.mp3 Externe Passing Juice
correct specification Interne Passing Juice
goodness-of-fit Interne Passing Juice
information matrix test Interne Passing Juice
LM101-057: How to Catch Spammers using Spectral Clustering Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/lm101-057.mp3 Externe Passing Juice
harvest bots Interne Passing Juice
harvesters Interne Passing Juice
LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications Interne Passing Juice
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Correlated Topic Models Interne Passing Juice
Information Matrix Tests Interne Passing Juice
Latent Dirichlet Allocation Interne Passing Juice
LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-055.mp3 Externe Passing Juice
learning machine Interne Passing Juice
MAP estimation Interne Passing Juice
maximum likelihood estimation Interne Passing Juice
LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN) Interne Passing Juice
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Automatic Essay Grading Interne Passing Juice
Latent Semantic Analysis Interne Passing Juice
Latent Semantic Indexing Interne Passing Juice
LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization) Interne Passing Juice
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Markov Field Interne Passing Juice
Metropolis-Hastings Interne Passing Juice
Monte Carlo Markov Chain Interne Passing Juice
LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-052.mp3 Externe Passing Juice
Function Approximation Interne Passing Juice
Supervised Learning Interne Passing Juice
kernel trick Interne Passing Juice
mercers theorem Interne Passing Juice
LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning [Rerun] Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-051.mp3 Externe Passing Juice
gaussian mixture model Interne Passing Juice
Hidden Units Interne Passing Juice
LM101-050: How to Use Linear Regression Software to Make Predictions (RERUN) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-050.mp3 Externe Passing Juice
free software Interne Passing Juice
iris data set Interne Passing Juice
linear regression Interne Passing Juice
LM101-049: How to Experiment with Lunar Lander Software Interne Passing Juice
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Adaptive gradient descent Interne Passing Juice
lunar lander Interne Passing Juice
supervised learning Interne Passing Juice
LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-048.mp3 Externe Passing Juice
control theory Interne Passing Juice
LM101-047: How to Build a Support Vector Machine to Classify Patterns (Rerun) Interne Passing Juice
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Logistic Regression Interne Passing Juice
Support Vector Machine Interne Passing Juice
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LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology) Interne Passing Juice
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Educational Technology Interne Passing Juice
educational technology; recurrent networks; item response theory; student learning Interne Passing Juice
LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images Interne Passing Juice
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LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference? Interne Passing Juice
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LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun) Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-043.mp3 Externe Passing Juice
LM101-042: What happened at the Monte Carlo Markov Chain Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference? Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-042.mp3 Externe Passing Juice
Gibbs Interne Passing Juice
MCMC Interne Passing Juice
LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial? Interne Passing Juice
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nips Interne Passing Juice
LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-040.mp3 Externe Passing Juice
LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)[Rerun] Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-039.mp3 Externe Passing Juice
Gibbs Sampler algorithm Interne Passing Juice
Markov random fields Interne Passing Juice
LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets (Educational Technology) Interne Passing Juice
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bayesian network Interne Passing Juice
educational technology Interne Passing Juice
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LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Interne Passing Juice
http://traffic.libsyn.com/learningmachines101/LM101-037.mp3 Externe Passing Juice
CAT Interne Passing Juice
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Mots-clefs

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how episode machines podcast machine using download learning summary new

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learning 103
how 56
episode 50
machine 42
podcast 33

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