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

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Titel

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

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Beskrivning

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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Egendom Innehåll
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
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Rubriker

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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Akaike Information Criterion Interna Passing Juice
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LM101-075: Can computers think? A Mathematician’s Response using a Turing Machine Argument (remix) Interna Passing Juice
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LM101-073: How to Build a Machine that Learns Checkers (remix) Interna Passing Juice
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Reinforcement Learning Interna Passing Juice
artificial intelligence Interna Passing Juice
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Monte Carlo Markov Chain Interna Passing Juice
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LM101-070: How to Identify Facial Emotion Expressions Using Stochastic Neighborhood Embedding Interna Passing Juice
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Clustering Algorithms Interna Passing Juice
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clustering Interna Passing Juice
Emotions Interna Passing Juice
Face Recognition Interna Passing Juice
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LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun) Interna Passing Juice
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Gibbs Sampler Interna Passing Juice
LM101-065: How to Design Gradient Descent Learning Machines (Rerun) Interna Passing Juice
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LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun) Interna Passing Juice
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Genetic Algorithms Interna Passing Juice
Darwin Natural Selection Interna Passing Juice
Evolution Interna Passing Juice
genetic algorithm Interna Passing Juice
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine Interna Passing Juice
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Expectation Maximization Interna Passing Juice
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LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine Interna Passing Juice
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LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms Interna Passing Juice
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LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear Interna Passing Juice
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Function Approximation Interna Passing Juice
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mercers theorem Interna Passing Juice
LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning [Rerun] Interna Passing Juice
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free software Interna Passing Juice
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LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)[Rerun] Interna Passing Juice
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Gibbs Sampler algorithm Interna Passing Juice
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