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DR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of the TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LM101-008: HOW TO REPRESENT BELIEFS USING PROBABILITY THEORY Episode Summary: This episode focusses upon how an intelligent system can represent beliefs about its environment using fuzzy measure theory. Probability theory is introduced as a special case of fuzzy measure theory which is consistent with classical laws of logicalinference.
LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). LM101-077: HOW TO CHOOSE THE BEST MODEL USING BIC To solve the model selection problem using the Bayesian Information Criterion we do the following. First, estimate the performance of your learning machine using one of the models using some training data. This is accomplished by finding the parameter values of your learning machine that make the observed data most probable. MATLAB COMPILER RUNTIME INSTALLER FOR MACSEE MORE ON LEARNINGMACHINES101.COM LEARNING MACHINES 101 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 FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation. This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. LEARNING MACHINES 101 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 LINEAR MACHINE LEARNING SOFTWARE (LINEAR REGRESSION SOFTWARE) Linear Machine Software Overview. The Linear Machine computer software takes as input a collection of input variables called “predictors” and a collection of output variables called “targets” which are arranged in a spreadsheet such that each row of the spreadsheet corresponds to a distinct data record. Two spreadsheets with thisformat
DR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of the TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LM101-008: HOW TO REPRESENT BELIEFS USING PROBABILITY THEORY Episode Summary: This episode focusses upon how an intelligent system can represent beliefs about its environment using fuzzy measure theory. Probability theory is introduced as a special case of fuzzy measure theory which is consistent with classical laws of logicalinference.
LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). LM101-077: HOW TO CHOOSE THE BEST MODEL USING BIC To solve the model selection problem using the Bayesian Information Criterion we do the following. First, estimate the performance of your learning machine using one of the models using some training data. This is accomplished by finding the parameter values of your learning machine that make the observed data most probable. MATLAB COMPILER RUNTIME INSTALLER FOR MACSEE MORE ON LEARNINGMACHINES101.COM FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 How to Run Executable Software on Windows or MAC OS-X. In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation.This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, trade FAQ - LEARNING MACHINES 101 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 learningwill be
DR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of theMACHINE LEARNING
About Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terroristbombs, perform
LUNAR LANDER SOFTWARE Lunar Lander Software Overview. “manual”, “autopilot”, “autopilot-nofuel”, and “autopilot-zero-learningrate”. Manual: In this mode, you have the option to try a manual landing the lunar lander. and (ii) discover a method for landing the lunar lander so that the lander behavior is LM101-002: HOW TO BUILD A MACHINE THAT LEARNS TO PLAY CHECKERS Podcast: Play in new window | Download | Embed Episode Summary: In this episode, we explain how to build a machine that learns to play checkers. The solution to this problem involves several key ideas which are fundamental to building systems which are artificiallyintelligent.
LM101-049: HOW TO EXPERIMENT WITH LUNAR LANDER SOFTWARE LM101-049: How to Experiment with Lunar Lander Software Episode Summary: In this episode we continue the discussion of learning when the actions of the learning machine can alter the characteristics of the learning machine’s statistical environment. We describe how to download free lunar lander software so you can experiment with an autopilot for a lunar lander module that Read More » %HOW TO REPRESENT KNOWLEDGE USING SET THEORY This particular podcast covers the material in Chapter 2 of my new book “Statistical Machine Learning: A unified framework” with expected publication date May 2020. In this episode we discuss Chapter 2 of my new book, which discusses how to represent knowledge using set theory notation. Chapter 2 is titled “Set Theory for ConceptModeling”.
LM101-076: HOW TO CHOOSE THE BEST MODEL USING AIC OR GAIC In this episode, we explain how to use the Akaike Information Criterion (AIC) to pick the model with the best generalization performance using only training data. The precise semantic interpretation of the Akaike Information Criterion (AIC) is provided, explicit assumptions are provided for the AIC and GAIC to be valid, and explicit formulas are provided for the AIC and GAIC so they can be LEARNING MACHINES 101 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 FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation. This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. LEARNING MACHINES 101 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 LINEAR MACHINE LEARNING SOFTWARE (LINEAR REGRESSION SOFTWARE) Linear Machine Software Overview. The Linear Machine computer software takes as input a collection of input variables called “predictors” and a collection of output variables called “targets” which are arranged in a spreadsheet such that each row of the spreadsheet corresponds to a distinct data record. Two spreadsheets with thisformat
DR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of the TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LM101-008: HOW TO REPRESENT BELIEFS USING PROBABILITY THEORY Episode Summary: This episode focusses upon how an intelligent system can represent beliefs about its environment using fuzzy measure theory. Probability theory is introduced as a special case of fuzzy measure theory which is consistent with classical laws of logicalinference.
LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). LM101-077: HOW TO CHOOSE THE BEST MODEL USING BIC To solve the model selection problem using the Bayesian Information Criterion we do the following. First, estimate the performance of your learning machine using one of the models using some training data. This is accomplished by finding the parameter values of your learning machine that make the observed data most probable. MATLAB COMPILER RUNTIME INSTALLER FOR MACSEE MORE ON LEARNINGMACHINES101.COM LEARNING MACHINES 101 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 FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation. This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. LEARNING MACHINES 101 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 LINEAR MACHINE LEARNING SOFTWARE (LINEAR REGRESSION SOFTWARE) Linear Machine Software Overview. The Linear Machine computer software takes as input a collection of input variables called “predictors” and a collection of output variables called “targets” which are arranged in a spreadsheet such that each row of the spreadsheet corresponds to a distinct data record. Two spreadsheets with thisformat
DR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of the TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LM101-008: HOW TO REPRESENT BELIEFS USING PROBABILITY THEORY Episode Summary: This episode focusses upon how an intelligent system can represent beliefs about its environment using fuzzy measure theory. Probability theory is introduced as a special case of fuzzy measure theory which is consistent with classical laws of logicalinference.
LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). LM101-077: HOW TO CHOOSE THE BEST MODEL USING BIC To solve the model selection problem using the Bayesian Information Criterion we do the following. First, estimate the performance of your learning machine using one of the models using some training data. This is accomplished by finding the parameter values of your learning machine that make the observed data most probable. MATLAB COMPILER RUNTIME INSTALLER FOR MACSEE MORE ON LEARNINGMACHINES101.COM FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 How to Run Executable Software on Windows or MAC OS-X. In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation.This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, trade FAQ - LEARNING MACHINES 101 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 learningwill be
DR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of theMACHINE LEARNING
About Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terroristbombs, perform
LUNAR LANDER SOFTWARE Lunar Lander Software Overview. “manual”, “autopilot”, “autopilot-nofuel”, and “autopilot-zero-learningrate”. Manual: In this mode, you have the option to try a manual landing the lunar lander. and (ii) discover a method for landing the lunar lander so that the lander behavior is LM101-002: HOW TO BUILD A MACHINE THAT LEARNS TO PLAY CHECKERS Podcast: Play in new window | Download | Embed Episode Summary: In this episode, we explain how to build a machine that learns to play checkers. The solution to this problem involves several key ideas which are fundamental to building systems which are artificiallyintelligent.
LM101-049: HOW TO EXPERIMENT WITH LUNAR LANDER SOFTWARE LM101-049: How to Experiment with Lunar Lander Software Episode Summary: In this episode we continue the discussion of learning when the actions of the learning machine can alter the characteristics of the learning machine’s statistical environment. We describe how to download free lunar lander software so you can experiment with an autopilot for a lunar lander module that Read More » %HOW TO REPRESENT KNOWLEDGE USING SET THEORY This particular podcast covers the material in Chapter 2 of my new book “Statistical Machine Learning: A unified framework” with expected publication date May 2020. In this episode we discuss Chapter 2 of my new book, which discusses how to represent knowledge using set theory notation. Chapter 2 is titled “Set Theory for ConceptModeling”.
LM101-076: HOW TO CHOOSE THE BEST MODEL USING AIC OR GAIC In this episode, we explain how to use the Akaike Information Criterion (AIC) to pick the model with the best generalization performance using only training data. The precise semantic interpretation of the Akaike Information Criterion (AIC) is provided, explicit assumptions are provided for the AIC and GAIC to be valid, and explicit formulas are provided for the AIC and GAIC so they can be FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation. This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large.MCR INSTALLER
MCR Installer for Machine Learning SoftwareDR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of theMACHINE LEARNING
About Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terroristbombs, perform
TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LM101-078: CH0: HOW TO BECOME A MACHINE LEARNING EXPERT To identify this special series of episodes about my new book I am introducing a prefix to the title of each episode which indexes a particular book chapter. This episode is a commentary on the book’s preface so I am using the notation: LM101-078: Ch0 as a prefix. In this first episode, we discuss possible ways one can become a machine LM101-002: HOW TO BUILD A MACHINE THAT LEARNS TO PLAY CHECKERS Podcast: Play in new window | Download | Embed Episode Summary: In this episode, we explain how to build a machine that learns to play checkers. The solution to this problem involves several key ideas which are fundamental to building systems which are artificiallyintelligent.
LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). LM101-076: HOW TO CHOOSE THE BEST MODEL USING AIC OR GAIC In this episode, we explain how to use the Akaike Information Criterion (AIC) to pick the model with the best generalization performance using only training data. The precise semantic interpretation of the Akaike Information Criterion (AIC) is provided, explicit assumptions are provided for the AIC and GAIC to be valid, and explicit formulas are provided for the AIC and GAIC so they can be MATLAB COMPILER RUNTIME INSTALLER FOR MACSEE MORE ON LEARNINGMACHINES101.COM FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation. This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large.MCR INSTALLER
MCR Installer for Machine Learning SoftwareDR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of theMACHINE LEARNING
About Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terroristbombs, perform
TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LM101-078: CH0: HOW TO BECOME A MACHINE LEARNING EXPERT To identify this special series of episodes about my new book I am introducing a prefix to the title of each episode which indexes a particular book chapter. This episode is a commentary on the book’s preface so I am using the notation: LM101-078: Ch0 as a prefix. In this first episode, we discuss possible ways one can become a machine LM101-002: HOW TO BUILD A MACHINE THAT LEARNS TO PLAY CHECKERS Podcast: Play in new window | Download | Embed Episode Summary: In this episode, we explain how to build a machine that learns to play checkers. The solution to this problem involves several key ideas which are fundamental to building systems which are artificiallyintelligent.
LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). LM101-076: HOW TO CHOOSE THE BEST MODEL USING AIC OR GAIC In this episode, we explain how to use the Akaike Information Criterion (AIC) to pick the model with the best generalization performance using only training data. The precise semantic interpretation of the Akaike Information Criterion (AIC) is provided, explicit assumptions are provided for the AIC and GAIC to be valid, and explicit formulas are provided for the AIC and GAIC so they can be MATLAB COMPILER RUNTIME INSTALLER FOR MACSEE MORE ON LEARNINGMACHINES101.COM LEARNING MACHINES 101 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 FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 How to Run Executable Software on Windows or MAC OS-X. In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation.This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, trade LEARNING MACHINES 101 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 CONTACT US - LEARNING MACHINES 101 Contact Us at Learning Machines 101 (www.learningmachines101.com) or at LINKED IN (Statistical Machine Learning Forum Group) for questionsand comments!
MACHINE LEARNING BOOK REVIEWS AAfter Digital: Computation as Done by Brains and MachinesJames A. AndersonDDeep LearningIan Goodfellow, Yoshua Bengio, and Aaron CourvileMMarkov Logic: An Interface Layer for Artificial IntelligencePedro Domingos and Daniel LowdPPattern Recognition and Machine LearningChristopher BishopQQuest for Artificial Intelligence,TheNils J. Nilsson.
LM101-078: CH0: HOW TO BECOME A MACHINE LEARNING EXPERT To identify this special series of episodes about my new book I am introducing a prefix to the title of each episode which indexes a particular book chapter. This episode is a commentary on the book’s preface so I am using the notation: LM101-078: Ch0 as a prefix. In this first episode, we discuss possible ways one can become a machine LM101-002: HOW TO BUILD A MACHINE THAT LEARNS TO PLAY CHECKERS Podcast: Play in new window | Download | Embed Episode Summary: In this episode, we explain how to build a machine that learns to play checkers. The solution to this problem involves several key ideas which are fundamental to building systems which are artificiallyintelligent.
%HOW TO REPRESENT KNOWLEDGE USING SET THEORY This particular podcast covers the material in Chapter 2 of my new book “Statistical Machine Learning: A unified framework” with expected publication date May 2020. In this episode we discuss Chapter 2 of my new book, which discusses how to represent knowledge using set theory notation. Chapter 2 is titled “Set Theory for ConceptModeling”.
LM101-077: HOW TO CHOOSE THE BEST MODEL USING BIC To solve the model selection problem using the Bayesian Information Criterion we do the following. First, estimate the performance of your learning machine using one of the models using some training data. This is accomplished by finding the parameter values of your learning machine that make the observed data most probable. FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation. This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, tradeBOOK REVIEWS
Visit World Health Organization COVID-19 Solidarity Response Fund Website. www.who.int. Submit a Review to ITUNES! CONTACT US - LEARNING MACHINES 101 Contact Us at Learning Machines 101 (www.learningmachines101.com) or at LINKED IN (Statistical Machine Learning Forum Group) for questionsand comments!
MCR INSTALLER
MCR Installer for Machine Learning SoftwareDR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of theMACHINE LEARNING
About Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terroristbombs, perform
TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LUNAR LANDER SOFTWARE LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation. This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, tradeBOOK REVIEWS
Visit World Health Organization COVID-19 Solidarity Response Fund Website. www.who.int. Submit a Review to ITUNES! CONTACT US - LEARNING MACHINES 101 Contact Us at Learning Machines 101 (www.learningmachines101.com) or at LINKED IN (Statistical Machine Learning Forum Group) for questionsand comments!
MCR INSTALLER
MCR Installer for Machine Learning SoftwareDR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of theMACHINE LEARNING
About Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terroristbombs, perform
TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LUNAR LANDER SOFTWARE LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, tradeBOOK REVIEWS
Visit World Health Organization COVID-19 Solidarity Response Fund Website. www.who.int. Submit a Review to ITUNES! CONTACT US - LEARNING MACHINES 101 Contact Us at Learning Machines 101 (www.learningmachines101.com) or at LINKED IN (Statistical Machine Learning Forum Group) for questionsand comments!
LEARNING MACHINES 101 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 FAQ - LEARNING MACHINES 101 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 learningwill be
LUNAR LANDER SOFTWARE Lunar Lander Software Overview. “manual”, “autopilot”, “autopilot-nofuel”, and “autopilot-zero-learningrate”. Manual: In this mode, you have the option to try a manual landing the lunar lander. and (ii) discover a method for landing the lunar lander so that the lander behavior is MODEL SELECTION ARCHIVES Podcast: Play in new window | Download | Embed Episode Summary: In this episode, we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and emphasize how this semantic interpretation is fundamentally different from AIC (Akaike Information Criterion) model selection methods. LM101-078: CH0: HOW TO BECOME A MACHINE LEARNING EXPERT To identify this special series of episodes about my new book I am introducing a prefix to the title of each episode which indexes a particular book chapter. This episode is a commentary on the book’s preface so I am using the notation: LM101-078: Ch0 as a prefix. In this first episode, we discuss possible ways one can become a machine LM101-082: CH4: HOW TO ANALYZE AND DESIGN LINEAR MACHINES The objective of Chapter 4 is to introduce advanced matrix operators and the singular value decomposition method for supporting the analysis and design of linear machine learning algorithms. More specifically, Chapter 4 begins with a review of basic matrix operations and definitions while introducing more advanced matrixoperators for
%HOW TO REPRESENT KNOWLEDGE USING SET THEORY This particular podcast covers the material in Chapter 2 of my new book “Statistical Machine Learning: A unified framework” with expected publication date May 2020. In this episode we discuss Chapter 2 of my new book, which discusses how to represent knowledge using set theory notation. Chapter 2 is titled “Set Theory for ConceptModeling”.
FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation. This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, tradeBOOK REVIEWS
Visit World Health Organization COVID-19 Solidarity Response Fund Website. www.who.int. Submit a Review to ITUNES! CONTACT US - LEARNING MACHINES 101 Contact Us at Learning Machines 101 (www.learningmachines101.com) or at LINKED IN (Statistical Machine Learning Forum Group) for questionsand comments!
MCR INSTALLER
MCR Installer for Machine Learning SoftwareDR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of theMACHINE LEARNING
About Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terroristbombs, perform
TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LUNAR LANDER SOFTWARE LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation. This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, tradeBOOK REVIEWS
Visit World Health Organization COVID-19 Solidarity Response Fund Website. www.who.int. Submit a Review to ITUNES! CONTACT US - LEARNING MACHINES 101 Contact Us at Learning Machines 101 (www.learningmachines101.com) or at LINKED IN (Statistical Machine Learning Forum Group) for questionsand comments!
MCR INSTALLER
MCR Installer for Machine Learning SoftwareDR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of theMACHINE LEARNING
About Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terroristbombs, perform
TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LUNAR LANDER SOFTWARE LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, tradeBOOK REVIEWS
Visit World Health Organization COVID-19 Solidarity Response Fund Website. www.who.int. Submit a Review to ITUNES! CONTACT US - LEARNING MACHINES 101 Contact Us at Learning Machines 101 (www.learningmachines101.com) or at LINKED IN (Statistical Machine Learning Forum Group) for questionsand comments!
LEARNING MACHINES 101 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 FAQ - LEARNING MACHINES 101 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 learningwill be
LUNAR LANDER SOFTWARE Lunar Lander Software Overview. “manual”, “autopilot”, “autopilot-nofuel”, and “autopilot-zero-learningrate”. Manual: In this mode, you have the option to try a manual landing the lunar lander. and (ii) discover a method for landing the lunar lander so that the lander behavior is MODEL SELECTION ARCHIVES Podcast: Play in new window | Download | Embed Episode Summary: In this episode, we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and emphasize how this semantic interpretation is fundamentally different from AIC (Akaike Information Criterion) model selection methods. LM101-078: CH0: HOW TO BECOME A MACHINE LEARNING EXPERT To identify this special series of episodes about my new book I am introducing a prefix to the title of each episode which indexes a particular book chapter. This episode is a commentary on the book’s preface so I am using the notation: LM101-078: Ch0 as a prefix. In this first episode, we discuss possible ways one can become a machine LM101-082: CH4: HOW TO ANALYZE AND DESIGN LINEAR MACHINES The objective of Chapter 4 is to introduce advanced matrix operators and the singular value decomposition method for supporting the analysis and design of linear machine learning algorithms. More specifically, Chapter 4 begins with a review of basic matrix operations and definitions while introducing more advanced matrixoperators for
%HOW TO REPRESENT KNOWLEDGE USING SET THEORY This particular podcast covers the material in Chapter 2 of my new book “Statistical Machine Learning: A unified framework” with expected publication date May 2020. In this episode we discuss Chapter 2 of my new book, which discusses how to represent knowledge using set theory notation. Chapter 2 is titled “Set Theory for ConceptModeling”.
FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation. This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, tradeBOOK REVIEWS
Visit World Health Organization COVID-19 Solidarity Response Fund Website. www.who.int. Submit a Review to ITUNES! CONTACT US - LEARNING MACHINES 101 Contact Us at Learning Machines 101 (www.learningmachines101.com) or at LINKED IN (Statistical Machine Learning Forum Group) for questionsand comments!
MCR INSTALLER
MCR Installer for Machine Learning SoftwareDR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of theMACHINE LEARNING
About Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terroristbombs, perform
TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LUNAR LANDER SOFTWARE LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). FREE SOFTWARE FOR MACHINE LEARNING BY LEARNING MACHINES 101 In order to run executable software provided on this website, you must download and install either the Windows MCR Library Installer Version 2011b 32-bits or the MAC OS-X MCR Library Installer Version 2012a 64-bits. This is a one-time installation. This file is approximately 300 megabytes (1/3 of a gigabyte) so it is very large. ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, tradeBOOK REVIEWS
Visit World Health Organization COVID-19 Solidarity Response Fund Website. www.who.int. Submit a Review to ITUNES! CONTACT US - LEARNING MACHINES 101 Contact Us at Learning Machines 101 (www.learningmachines101.com) or at LINKED IN (Statistical Machine Learning Forum Group) for questionsand comments!
MCR INSTALLER
MCR Installer for Machine Learning SoftwareDR. RICHARD GOLDEN
Currently, Dr. Richard M. Golden is a full-time Professor of Cognitive Science and Electrical Engineering. Dr. Richard M. Golden was a member of the Editorial Board of the Journal of Mathematical Psychology from 1996-2011, a member of the Editorial Board of Neural Processing Letters from 1999-2004, a member of the Editorial Board of theMACHINE LEARNING
About Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terroristbombs, perform
TURING TEST ARCHIVES Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer’s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called “artificially intelligent”. LUNAR LANDER SOFTWARE LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE Podcast: Play in new window | Download | Embed LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). ABOUT LEARNING MACHINES 101 Learning Machines 101. Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. For example, artificially intelligent systems recognize and produce speech, control bionic limbs for wounded warriors, automatically recognize and sort pictures of family and friends, disarm terrorist bombs, perform medical diagnoses, detect banking fraud and spam, tradeBOOK REVIEWS
Visit World Health Organization COVID-19 Solidarity Response Fund Website. www.who.int. Submit a Review to ITUNES! CONTACT US - LEARNING MACHINES 101 Contact Us at Learning Machines 101 (www.learningmachines101.com) or at LINKED IN (Statistical Machine Learning Forum Group) for questionsand comments!
LEARNING MACHINES 101 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 FAQ - LEARNING MACHINES 101 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 learningwill be
LUNAR LANDER SOFTWARE Lunar Lander Software Overview. “manual”, “autopilot”, “autopilot-nofuel”, and “autopilot-zero-learningrate”. Manual: In this mode, you have the option to try a manual landing the lunar lander. and (ii) discover a method for landing the lunar lander so that the lander behavior is MODEL SELECTION ARCHIVES Podcast: Play in new window | Download | Embed Episode Summary: In this episode, we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and emphasize how this semantic interpretation is fundamentally different from AIC (Akaike Information Criterion) model selection methods. LM101-078: CH0: HOW TO BECOME A MACHINE LEARNING EXPERT To identify this special series of episodes about my new book I am introducing a prefix to the title of each episode which indexes a particular book chapter. This episode is a commentary on the book’s preface so I am using the notation: LM101-078: Ch0 as a prefix. In this first episode, we discuss possible ways one can become a machine LM101-082: CH4: HOW TO ANALYZE AND DESIGN LINEAR MACHINES The objective of Chapter 4 is to introduce advanced matrix operators and the singular value decomposition method for supporting the analysis and design of linear machine learning algorithms. More specifically, Chapter 4 begins with a review of basic matrix operations and definitions while introducing more advanced matrixoperators for
%HOW TO REPRESENT KNOWLEDGE USING SET THEORY This particular podcast covers the material in Chapter 2 of my new book “Statistical Machine Learning: A unified framework” with expected publication date May 2020. In this episode we discuss Chapter 2 of my new book, which discusses how to represent knowledge using set theory notation. Chapter 2 is titled “Set Theory for ConceptModeling”.
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LM101-078: Ch0: How to Become a Machine Learning Expert*
LM101-079: Ch1: How to View Learning as Risk Minimization*
LM101-078: Ch0: How to Become a Machine Learning Expert*
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LM101-079: CH1: HOW TO VIEW LEARNING AS RISK MINIMIZATIONAudio Player
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Episode Summary: This particular podcast covers the material in Chapter 1 of my new (unpublished) book “Statistical Machine Learning: A unified framework”. In this episode we discuss Chapter 1 of my new book, which shows how supervised, unsupervised, and reinforcement learning algorithms can be viewed as special cases of a general empirical risk minimization framework. This is useful… ReadMore »
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BOOK SMLBOOK
Topic
book empirical risk reinforcement learning LM101-078: CH0: HOW TO BECOME A MACHINE LEARNING EXPERTAudio Player
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This particular podcast is the initial episode in a new special series of episodes designed to provide commentary on a new book that I am in the process of writing. In this episode we discuss books, software, courses, and podcasts designed to help you become a machine learningexpert!
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BOOK SMLBOOK
machine learning books machine learning mathematics machine learning software LM101-077: HOW TO CHOOSE THE BEST MODEL USING BICAudio Player
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Episode Summary: In this episode, we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and emphasize how this semantic interpretation is fundamentally different from AIC (Akaike Information Criterion) model selection methods. Briefly, BIC is used to estimate the probability of the training data given the probability model, while AIC is used to estimate out-of-sample prediction… Read More »*
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Generalization PerformanceMachine Learning
Model
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Topic
Bayesian Information Criterion BIC Marginal Likelihood LM101-076: HOW TO CHOOSE THE BEST MODEL USING AIC OR GAICAudio Player
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Episode Summary: In this episode, we explain the proper semantic interpretation of the Akaike Information Criterion (AIC) and the Generalized Akaike Information Criterion (GAIC) for the purpose of picking the best model for a given set of training data. The precise semantic interpretation of these model selection criteria is provided, explicit assumptions are provided for the AIC and… Read More »*
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Generalization PerformanceModel Selection
Topic
AIC Akaike InformationCriterion
cross-validation
LM101-075: CAN COMPUTERS THINK? A MATHEMATICIAN’S RESPONSE USING A TURING MACHINE ARGUMENT (REMIX) LM101-075: Can computers think? A Mathematician’s Response using a Turing Machine Argument (remix) Episode Summary: In this episode, we explore the question of what can computers do as well as what computers can’t do using the Turing Machine argument. Specifically, we discuss the computational limits of computers and raise the question of whether such limits pertain to biological… Read More »*
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LM101-074: HOW TO REPRESENT KNOWLEDGE USING LOGICAL RULES (REMIX)Audio Player
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LM101-074: How to Represent Knowledge using Logical Rules (remix) Episode Summary: In this episode we will learn how to use “rules” to represent knowledge. We discuss how this works in practice and we explain how these ideas are implemented in a special architecture called the production system. The challenges of representing knowledge using rules are also discussed. Specifically,… Read More »*
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Features
Rule-based Inferencefeature vector
features
logic
LM101-073: HOW TO BUILD A MACHINE THAT LEARNS CHECKERS (REMIX)Audio Player
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LM101-073: How to Build a Machine that Learns Checkers (remix) Episode Summary: This is a remix of the original second episode Learning Machines 101 which describes in a little more detail how the computer program that Arthur Samuel developed in 1959 learned to play checkers by itself without human intervention using a mixture of classical artificial intelligence search… Read More »*
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Machine Learning
Reinforcement LearningTopic
artificial intelligence Artificial Neural NetworksEvaluation Function
LM101-072: WELCOME TO THE BIG ARTIFICIAL INTELLIGENCE MAGIC SHOW! (LM101-001+LM101-002 REMIX)Audio Player
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LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (LM101-001+LM101-002 remix) Episode Summary: This podcast is basically a remix of the first and second episodes of Learning Machines 101 and is intended to serve as the new introduction to the Learning Machines 101 podcast series. The search for common organizing principles which could support the foundations of machine… Read More »*
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Machine Learning
Topic
learning machines 101machine learning
Neural
Networks
LM101-071: HOW TO MODEL COMMON SENSE KNOWLEDGE USING FIRST-ORDER LOGIC AND MARKOV LOGIC NETSAudio Player
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LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets Episode Summary: In this podcast, we provide some insights into the complexity of common sense. First, we discuss the importance of building common sense into learning machines. Second, we discuss how first-order logic can be used to represent common sense knowledge. Third, we describe… Read More »*
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Constraint Satisfaction Monte Carlo Markov Chain Probabilistic Inference Rule-based Inference common-sense knowledgeCYC
CYCL
LM101-070: HOW TO IDENTIFY FACIAL EMOTION EXPRESSIONS USING STOCHASTIC NEIGHBORHOOD EMBEDDINGAudio Player
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LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding Episode Summary: This 70th episode of Learning Machines 101 we discuss how to identify facial emotion expressions in images using an advanced clustering technique called Stochastic Neighborhood Embedding. We discuss the concept of recognizing facial emotions in images including applications to problems such as: improving… Read More »*
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Clustering Algorithms Constraint Satisfaction Unsupervised Learningclustering
Emotions Face
Recognition
LM101-069: WHAT HAPPENED AT THE 2017 NEURAL INFORMATION PROCESSINGSYSTEMS CONFERENCE?
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LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference? Episode Summary: This 69th episode of Learning Machines 101 provides a short overview of the 2017 Neural Information Processing Systems conference with a focus on the development of methods for teaching learning machines rather than simply training them on examples. In addition, a book review of… Read More »*
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Book Review
Deep
Learning
Gradient Descent Learningcurricula neural
information processing systemsNIPS 2017
LM101-068: HOW TO DESIGN AUTOMATIC LEARNING RATE SELECTION FOR GRADIENT DESCENT TYPE MACHINE LEARNING ALGORITHMSAudio Player
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LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms Episode Summary: This 68th episode of Learning Machines 101 discusses a broad class of unsupervised, supervised, and reinforcement machine learning algorithms which iteratively update their parameter vector by adding a perturbation based upon all of the training data. This process is repeated, making… Read More »*
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Deep Learning
Gradient Descent LearningTopic
Backtracking Linesearchbatch learning
Convergence
Theorem
LM101-067: HOW TO USE EXPECTATION MAXIMIZATION TO LEARN CONSTRAINT SATISFACTION SOLUTIONS (RERUN) LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun) Episode Summary: In this episode we discuss how to learn to solve constraint satisfaction inference problems. The goal of the inference process is to infer the most probable values for unobservable variables. These constraints, however, can be learned from experience. Specifically, the important machine learning method… Read More »*
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Monte Carlo Markov ChainTopic
Boltzmann Machine
Constraint SatisfactionDreams
LM101-066: HOW TO SOLVE CONSTRAINT SATISFACTION PROBLEMS USING MCMCMETHODS (RERUN)
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LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun) Episode Summary: In this episode we discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered collection of complicated probabilistic constraints among a collection of variables. The goal of the inference process is to infer the most probable values of the…Read More »
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Monte Carlo Markov ChainTopic
Boltzmann Machine
Constraint SatisfactionGibbs Sampler
LM101-065: HOW TO DESIGN GRADIENT DESCENT LEARNING MACHINES (RERUN)Audio Player
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LM101-065: How to Design Gradient Descent Learning Machines (Rerun) Episode Summary: In this episode we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of deep learning and neural network learning algorithms. Show Notes: Hello everyone! Welcome to the sixteenth podcast in the podcast series Learning Machines 101. In this series… Read More »*
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Deep Learning
Gradient Descent LearningTopic
batch learning
gradient
descent
line search
LM101-064: STOCHASTIC MODEL SEARCH AND SELECTION WITH GENETICALGORITHMS (RERUN)
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LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun) Episode Summary: In this episode we explore the concept of evolutionary learning machines. That is, learning machines that reproduce themselves in the hopes of evolving into more intelligent and smarter learning machines. This is a rerun of Episode 24. Show Notes: Hello everyone! Welcome to the twenty-fourth podcastin… Read More »
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Genetic Algorithms
Darwin Natural SelectionEvolution genetic
algorithm
LM101-063: HOW TO TRANSFORM A SUPERVISED LEARNING MACHINE INTO A POLICY GRADIENT REINFORCEMENT LEARNING MACHINEAudio Player
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LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine Episode Summary: This 63rd episode of Learning Machines 101 discusses how to build reinforcement learning machines which become smarter with experience but do not use this acquired knowledge to modify their actions and behaviors. This episode explains how to build reinforcement learning machines… Read More »*
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Deep Learning
Reinforcement LearningTopic
Expectation Maximization Monte Carlo Expectation Maximizationpolicy gradient
LM101-062: HOW TO TRANSFORM A SUPERVISED LEARNING MACHINE INTO A VALUE FUNCTION REINFORCEMENT LEARNING MACHINEAudio Player
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LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine Episode Summary: This 62nd episode of Learning Machines 101 discusses how to design reinforcement learning machines using your knowledge of how to build supervised learning machines! Specifically, we focus on Value Function Reinforcement Learning Machines which estimate the unobservable total penalty associated with… Read More »*
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Deep Learning
Reinforcement LearningTopic
Deep Reinforcement LearningGame playing Q
learning
LM101-061: WHAT HAPPENED AT THE REINFORCEMENT LEARNING TUTORIAL?(RERUN)
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LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN) Episode Summary: This is the third of a short subsequence of podcasts providing a summary of events associated with Dr. Golden’s recent visit to the 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the field of Machine Learning. This episode reviews and discusses… Read More »*
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Deep Learning
Reinforcement LearningTopic
Deep Learning
off-policy
on-policy
LM101-060: HOW TO MONITOR MACHINE LEARNING ALGORITHMS USING ANOMALY DETECTION MACHINE LEARNING ALGORITHMSAudio Player
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LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms Episode Summary: This 60th episode of Learning Machines 101 discusses how one can use novelty detection or anomaly detection machine learning algorithms to monitor the performance of other machine learning algorithms deployed in real world environments. The episode is based upon a review of a… ReadMore »
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Unsupervised LearningAnodot Anomaly
Detection
Berlin Buzzwords
LM101-059: HOW TO PROPERLY INTRODUCE A NEURAL NETWORKAudio Player
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LM101-059: How to Properly Introduce a Neural Network Episode Summary: I discuss the concept of a “neural network” by providing some examples of recent successes in neural network machine learning algorithms and providing a historical perspective on the evolution of the neural network concept from its biological origins. Show Notes: Hello everyone! Welcome to the fifty-ninth podcast in… Read More »*
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Biological Neural NetworksDeep Learning
Software
biological neural networks Computational Neuroscience Convolutional Neural Networks LM101-058: HOW TO IDENTIFY HALLUCINATING LEARNING MACHINES USING SPECIFICATION ANALYSISAudio Player
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LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis Episode Summary: In this 58th episode of Learning Machines 101, I’ll be discussing an important new scientific breakthrough published just last week for the first time in the journal Econometrics in the special issue on model misspecification titled “Generalized Information Matrix Tests for Detecting Model Misspecification”. The article… Read More »*
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Generalization PerformanceTopic
correct specificationgoodness-of-fit
information
matrix test
LM101-057: HOW TO CATCH SPAMMERS USING SPECTRAL CLUSTERINGAudio Player
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LM101-057: How to Catch Spammers using Spectral Clustering Episode Summary: In this 57th episode, we explain how to use spectral cluster analysis unsupervised machine learning algorithms to catch internet criminals who try to steal your money electronically! Show Notes: Hello everyone! Welcome to the fifty-seventh podcast in the podcast series Learning Machines 101. In this series of podcasts… Read More*
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Topic
Unsupervised Learningclustering
harvest bots
harvesters
LM101-056: HOW TO BUILD GENERATIVE LATENT PROBABILISTIC TOPIC MODELS FOR SEARCH ENGINE AND RECOMMENDER SYSTEM APPLICATIONSAudio Player
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LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications Episode Summary: In this episode we discuss Latent Semantic Indexing type machine learning algorithms which have a probabilistic interpretation. We explain why such a probabilistic interpretation is important and discuss how such algorithms can be used in the design of document retrieval… Read More »*
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Probabilistic InferenceTopic
Unsupervised Learning Correlated Topic Models Information Matrix Tests Latent Dirichlet Allocation LM101-055: HOW TO LEARN STATISTICAL REGULARITIES USING MAP AND MAXIMUM LIKELIHOOD ESTIMATION (RERUN)Audio Player
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LM101-055: How to Learn Statistical Regularities using MAP and ML Estimation Episode Summary: In this rerun of Episode 10, we discuss fundamental principles of learning in statistical environments including the design of learning machines that can use prior knowledge to facilitate and guide the learning of statistical regularities. Show Notes: Hello everyone! Welcome to the tenth podcast in… Read More »*
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Generalization Performance Probabilistic InferenceTopic
learning machine
MAP
estimation
maximum likelihood estimation LM101-054: HOW TO BUILD SEARCH ENGINE AND RECOMMENDER SYSTEMS USING LATENT SEMANTIC ANALYSIS (RERUN)Audio Player
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LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN) Episode Summary: In this episode we explain how to build a search engine, automatically grade essays, and identify synonyms using Latent Semantic Analysis. Preamble: Welcome to the 54th Episode of Learning Machines 101 titled “How to Build a Search Engine, Automatically Grade Essays,… Read More »*
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Topic
Unsupervised Learning Automatic Essay Grading Latent Semantic Analysis Latent Semantic Indexing LM101-053: HOW TO ENHANCE LEARNING MACHINES WITH SWARM INTELLIGENCE (PARTICLE SWARM OPTIMIZATION)Audio Player
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LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization) Episode Summary: In this 53rd episode of Learning Machines 101, we introduce the concept of a Swarm Intelligence with respect to Particle Swarm Optimization Algorithms. The essential idea of “Swarm Intelligence” is that you have a group of individual entities which behave in a coordinated manner…Read More »
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Monte Carlo Markov ChainTopic
Markov Field
Metropolis-Hastings
Monte
Carlo Markov Chain
LM101-052: HOW TO USE THE KERNEL TRICK TO MAKE HIDDEN UNITS DISAPPEARAudio Player
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LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear Episode Summary: Today, we discuss a simple yet powerful idea which began popular in the machine learning literature in the 1990s which is called “The Kernel Trick”. The basic idea behind “The Kernel Trick” is that an impossible machine learning problem can be transformed into an… Read More »*
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Deep Learning
Features
Function ApproximationSupervised Learning
Topic
Deep Learning
kernel trick
mercers theorem
LM101-051: HOW TO USE RADIAL BASIS FUNCTION PERCEPTRON SOFTWARE FORSUPERVISED LEARNING
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LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning Episode Summary: In this episode we describe how to download and use free nonlinear machine learning software for implementing a Perceptron learning machine with a single layer of Radial Basis Function hidden units for the purposes of supervised learning. Show Notes: Welcome to the 51st podcast… ReadMore »
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Deep Learning
Software
Supervised Learning
Topic
Deep Learning
gaussian mixture modelHidden Units
LM101-050: HOW TO USE LINEAR REGRESSION SOFTWARE TO MAKE PREDICTIONS(RERUN)
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LM101-050: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software) Episode Summary: In this episode we describe how to download and use free linear machine learning software to make predictions for classifying flower species using a famous machine learning data set. This is a RERUN of Episode 13. Show Notes: Hello everyone! Welcome to… Read More »*
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Software
Supervised Learning
Topic
free software
iris data set
linear regression
LM101-049: HOW TO EXPERIMENT WITH LUNAR LANDER SOFTWAREAudio Player
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LM101-049: How to Experiment with Lunar Lander Software Episode Summary: In this episode we continue the discussion of learning when the actions of the learning machine can alter the characteristics of the learning machine’s statistical environment. We describe how to download free lunar lander software so you can experiment with an autopilot for a lunar lander module that… Read More »*
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Gradient Descent Learning Reinforcement Learning Adaptive gradient descentlunar lander
supervised learning
LM101-048: HOW TO BUILD A LUNAR LANDER AUTOPILOT LEARNING MACHINE(RERUN)
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LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun) Episode Summary: In this episode we consider the problem of learning when the actions of the learning machine can alter the characteristics of the learning machine’s statistical environment. We illustrate the solution to this problem by designing an autopilot for a lunar lander module that learns from… Read More »*
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Reinforcement LearningTopic
control theory
lunar lander
reinforcement
learning
LM101-047: HOW TO BUILD A SUPPORT VECTOR MACHINE TO CLASSIFY PATTERNS(RERUN)
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LM101-047: How To Build a Support Vector Machine to Classify Patterns (Rerun) Episode Summary: In this RERUN of the 32nd episode of Learning Machines 101, we introduce the concept of a Support Vector Machine. We explain how to estimate the parameters of such machines to classify a pattern vector as a member of one of two categories… ReadMore »
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Supervised Learning
Topic
Logistic Regression
Support
Vector Machine
svm
LM101-046: HOW TO OPTIMIZE STUDENT LEARNING USING RECURRENT NEURAL NETWORKS (EDUCATIONAL TECHNOLOGY)Audio Player
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LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology) Episode Summary: In this episode, we briefly review Item Response Theory and Bayesian Network Theory methods for the assessment and optimization of student learning and then describe a poster presented on the first day of the Neural Information Processing Systems conference in December 2015 in Montreal… Read More »*
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Deep Learning
Educational TechnologyTopic
educational technology; recurrent networks; item response theory;student learning
LM101-045: HOW TO BUILD A DEEP LEARNING MACHINE FOR ANSWERING QUESTIONS ABOUT IMAGESAudio Player
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LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images Episode Summary: This is the fourth of a short subsequence of podcasts which provides a summary of events associated with Dr. Golden’s recent visit to the 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the field of Machine Learning. This… Read More »*
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Deep Learning
Recurrent Networks
Turing Test
Deep Learning
recurrent networks
Turing
Test
LM101-044: WHAT HAPPENED AT THE DEEP REINFORCEMENT LEARNING TUTORIAL AT THE 2015 NEURAL INFORMATION PROCESSING SYSTEMS CONFERENCE?Audio Player
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LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference? Episode Summary: This is the third of a short subsequence of podcasts providing a summary of events associated with Dr. Golden’s recent visit to the 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the field… Read More »*
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Deep Learning
Reinforcement LearningDeep Learning
neural information processing systemsQ learning
LM101-043: HOW TO LEARN A MONTE CARLO MARKOV CHAIN TO SOLVE CONSTRAINT SATISFACTION PROBLEMS (RERUN)Audio Player
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LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22) Welcome to the 43rd Episode of Learning Machines 101! We are currently presenting a subsequence of episodes covering the events of the recent Neural Information Processing Systems Conference. However, this week will digress with a rerun of Episode 22 which… Read More »*
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Constraint Satisfaction Probabilistic InferenceTopic
Unsupervised LearningBoltzmann Machine
Constraint SatisfactionDreams
LM101-042: WHAT HAPPENED AT THE MONTE CARLO MARKOV CHAIN INFERENCE METHODS TUTORIAL AT THE 2015 NEURAL INFORMATION PROCESSING SYSTEMSCONFERENCE?
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LM101-042: What happened at the Monte Carlo Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference? Episode Summary: This is the second of a short subsequence of podcasts providing a summary of events associated with Dr. Golden’s recent visit to the 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the… Read More »*
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Constraint Satisfaction Probabilistic InferenceTopic
Constraint SatisfactionGibbs MCMC
LM101-041: WHAT HAPPENED AT THE 2015 NEURAL INFORMATION PROCESSING SYSTEMS DEEP LEARNING TUTORIAL?Audio Player
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LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial? Episode Summary: This is the first of a short subsequence of podcasts which provides a summary of events at the recent 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the field of Machine Learning. This episode introduces the Neural… Read More »*
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Deep Learning
Gradient Descent LearningRecurrent Networks
Supervised Learning
Topic
Unsupervised LearningDeep Learning
neural information processing systemsnips
LM101-040: HOW TO BUILD A SEARCH ENGINE, AUTOMATICALLY GRADE ESSAYS, AND IDENTIFY SYNONYMS USING LATENT SEMANTIC ANALYSISAudio Player
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LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis Episode Summary: In this episode we explain how to build a search engine, automatically grade essays, and identify synonyms using Latent Semantic Analysis. Show Notes: Hello everyone! Welcome to the fortieth podcast in the podcast series Learning Machines 101. In this… Read More »*
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Educational Technology Generalization Performance Unsupervised Learning Automatic Essay Grading Latent Semantic Analysis Latent Semantic Indexing LM101-039: HOW TO SOLVE LARGE COMPLEX CONSTRAINT SATISFACTION PROBLEMS (MONTE CARLO MARKOV CHAIN)Audio Player
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LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain) Episode Summary: In this episode we discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered collection of complicated probabilistic constraints among a collection of variables. The goal of the inference process is to infer the most probable values… Read More »*
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Constraint SatisfactionTopic
Constraint Satisfaction Gibbs Sampler algorithm Markov random fields LM101-038: HOW TO MODEL KNOWLEDGE SKILL GROWTH OVER TIME USING BAYESIAN NETS (EDUCATIONAL TECHNOLOGY)Audio Player
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LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets Episode Summary: In this episode, we examine the problem of developing an advanced artificially intelligent technology which is capable of tracking knowledge growth in students in real-time, representing the knowledge state of a student a skill profile, and automatically defining the concept of a skill without… Read More »*
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Educational Technologybayesian network
educational technologyhidden Markov model
LM101-037: HOW TO BUILD A SMART COMPUTERIZED ADAPTIVE TESTING MACHINE USING ITEM RESPONSE THEORYAudio Player
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LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). Suppose that you are teaching a student a particular target set of knowledge. Examples of such situations obviously occur in… Read More »*
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Educational TechnologyTopic
CAT Computerized
adaptive testing
educational technology LM101-036: HOW TO PREDICT THE FUTURE FROM THE DISTANT PAST USING RECURRENT NEURAL NETWORKSAudio Player
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LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks Episode Summary: In this episode, we discuss the problem of predicting the future from not only recent events but also from the distant past using Recurrent Neural Networks (RNNs). A example RNN is described which learns to label images with simple sentences. A learning… Read More »*
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Deep Learning
Gradient Descent LearningRecurrent Networks
Topic
ADAGRAD Deep
Learning
elman network
LM101-035: WHAT IS A NEURAL NETWORK AND WHAT IS A HOT DOG?Audio Player
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Episode Summary: In this episode, we address the important questions of “What is a neural network?” and “What is a hot dog?” by discussing human brains, neural networks that learn to play Atari video games, and rat brain neural networks. Show Notes: Hello everyone! Welcome to the thirty-fifth podcast in the podcast series Learning Machines 101. In this… Read More »*
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Biological Neural Networks Reinforcement LearningTopic
artificial neural network biological neural networksblue brain
LM101-034: HOW TO USE NONLINEAR MACHINE LEARNING SOFTWARE TO MAKE PREDICTIONS (FEEDFORWARD PERCEPTRONS WITH RADIAL BASISFUNCTIONS)
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LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions) Episode Summary: In this episode we describe how to download and use free nonlinear machine learning software which is more advanced than the linear machine software introduced in Episode 13. Show Notes: Welcome to the 34th podcast in the podcast series…Read More »
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Deep Learning
Gradient Descent LearningSoftware
Supervised Learning
gaussian mixture modelPerceptron
radial basis functions LM101-033: HOW TO USE LINEAR MACHINE LEARNING SOFTWARE TO MAKE PREDICTIONS (LINEAR REGRESSION SOFTWARE)Audio Player
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LM101-033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software) Episode Summary: In this episode we describe how to download and use free linear machine learning software to make predictions for classifying flower species using a famous machine learning data set. This is a RERUN of Episode 13. Show Notes: Hello everyone! Welcome… Read More »*
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Generalization PerformanceSoftware
Supervised Learning
LM101-032: HOW TO BUILD A SUPPORT VECTOR MACHINE TO CLASSIFY PATTERNSAudio Player
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LM101-032: How To Build a Support Vector Machine to Classify Patterns Episode Summary: In this 32nd episode of Learning Machines 101, we introduce the concept of a Support Vector Machine. We explain how to estimate the parameters of such machines to classify a pattern vector as a member of one of two categories as well as identify special…Read More »
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Supervised Learning
Topic
constrained optimization Lagrange MultipliersLogistic Regression
LM101-031: HOW TO ANALYZE AND DESIGN LEARNING RULES USING GRADIENT DESCENT METHODS (RERUN)Audio Player
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LM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN) Episode Summary: In this episode we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of machine learning algorithms. Show Notes: Hello everyone! Welcome to the sixteenth podcast in the podcast series Learning Machines 101. In this series… Read More »*
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Gradient Descent LearningTopic
batch learning
gradient
descent
line search
LM101-030: HOW TO IMPROVE DEEP LEARNING PERFORMANCE WITH ARTIFICIAL BRAIN DAMAGE (DROPOUT AND MODEL AVERAGING)Audio Player
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LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging) Episode Summary: Deep learning machine technology has rapidly developed over the past five years due in part to a variety of factors such as: better technology, convolutional net algorithms, rectified linear units, and a relatively new learning strategy called “dropout” in which hidden… ReadMore »
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Deep Learning
Features
Function ApproximationTopic
Convolutional Neural NetworksDeep Learning
Dropout
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