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

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Título

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

Cumprimento : 93

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Descrição

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.

Cumprimento : 777

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Propriedade Conteúdo
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

Cabeçalhos

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

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learning using summary episode new podcast machines download how machine

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how 56
episode 50
machine 42
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