Android Backstage, a podcast by and for Android developers. Hosted by developers from the Android engineering team, this show covers topics of interest to Android programmers, with in-depth discussions and interviews with engineers on the Android team at Google. Subscribe to Android Developers YouTube → https://goo.gle/AndroidDevs
…
continue reading
Podcast Player
141 subscribers
Checked 1y ago
Added six years ago
Content provided by Richard M. Golden, M.S.E.E., and B.S.E.E.. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Richard M. Golden, M.S.E.E., and B.S.E.E. or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
Similar to Learning Machines 101
The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
…
continue reading
BBC Radio 5 live’s award winning gaming podcast, discussing the world of video games and games culture.
…
continue reading
Africa-focused technology, digital and innovation ecosystem insight and commentary.
…
continue reading
Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
…
continue reading
Show notes are at https://stevelitchfield.com/sshow/chat.html
…
continue reading
Hanselminutes is Fresh Air for Developers. A weekly commute-time podcast that promotes fresh technology and fresh voices. Talk and Tech for Developers, Life-long Learners, and Technologists.
…
continue reading
Why do only 10% of companies succeed with AI? In this series by MIT SMR and BCG, we talk to the leaders who've achieved big wins with AI in their companies and learn how they did it. Hear what gets experts from companies like NASA, Github, and others excited to do their jobs every day and what they consider the keys to their success.
…
continue reading
The power of Data is undeniable. And unharnessed - it’s nothing but chaos. Making data your ally. Using it to lead with confidence and clarity. Host Jess Carter is solving problems in real-time to reveal what’s possible. Helping communities and people thrive. This is Data Driven Leadership, a show brought to you by Resultant.
…
continue reading
Material is a weekly discussion about the Google and Android universe. Your intrepid hosts try to answer the question, “What holds up the digital world?” The answer, so far, is that it’s Google all the way down. Hosted by Andy Ihnatko and Florence Ion.
…
continue reading
Player FM - Podcast App
Go offline with the Player FM app!
Go offline with the Player FM app!
))
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
Manage episode 230297536 series 2497400
Content provided by Richard M. Golden, M.S.E.E., and B.S.E.E.. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Richard M. Golden, M.S.E.E., and B.S.E.E. or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
In this episode of Learning Machines 101 (www.learningmachines101.com) 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 unobservable variables given the observable variables. Specifically, Monte Carlo Markov Chain ( MCMC ) methods are discussed.
85 episodes
Share
Manage episode 230297536 series 2497400
Content provided by Richard M. Golden, M.S.E.E., and B.S.E.E.. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Richard M. Golden, M.S.E.E., and B.S.E.E. or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
In this episode of Learning Machines 101 (www.learningmachines101.com) 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 unobservable variables given the observable variables. Specifically, Monte Carlo Markov Chain ( MCMC ) methods are discussed.
85 episodes
All episodes
×
L
Learning Machines 101
1 LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes 35:29
35:29 
Play Later 
Play Later 
Lists 
Like 
Liked35:29
Play Later Play Later Lists Like LikedThis 86th episode of Learning Machines 101 discusses the problem of assigning probabilities to a possibly infinite set of outcomes in a space-time continuum which characterizes our physical world. Such a set is called an “environmental event”. The machine learning algorithm uses information about the frequency of environmental events to support learning. If we want to study statistical machine learning, then we must be able to discuss how to represent and compute the probability of an environmental event. It is essential that we have methods for communicating probability concepts to other researchers, methods for calculating probabilities, and methods for calculating the expectation of specific environmental events. This episode discusses the challenges of assigning probabilities to events when we allow for the case of events comprised of an infinite number of outcomes. Along the way we introduce essential concepts for representing and computing probabilities using measure theory mathematical tools such as sigma fields, and the Radon-Nikodym probability density function. Near the end we also briefly discuss the intriguing Banach-Tarski paradox and how it motivates the development of some of these special mathematical tools. Check out: www.learningmachines101.com and www.statisticalmachinelearning.com for more information!!!…
L
Learning Machines 101
1 LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges 30:51
30:51 Play Later Play Later Lists Like Liked30:51
Play Later Play Later Lists Like LikedThis 85th episode of Learning Machines 101 discusses formal convergence guarantees for a broad class of machine learning algorithms designed to minimize smooth non-convex objective functions using batch learning methods. In particular, 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 a perturbation of the parameter vector based upon all of the training data until a parameter vector is generated which exhibits improved predictive performance. The magnitude of the perturbation at each learning iteration is called the “stepsize” or “learning rate” and the identity of the perturbation vector is called the “search direction”. Simple mathematical formulas are presented based upon research from the late 1960s by Philip Wolfe and G. Zoutendijk that ensure convergence of the generated sequence of parameter vectors. These formulas may be used as the basis for the design of artificially intelligent smart automatic learning rate selection algorithms. The material in this podcast is designed to provide an overview of Chapter 7 of my new book “Statistical Machine Learning” and is based upon material originally presented in Episode 68 of Learning Machines 101! Check out: www.learningmachines101.com for the show notes!!!…
L
Learning Machines 101
1 LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems 33:13
33:13 Play Later Play Later Lists Like Liked33:13
Play Later Play Later Lists Like LikedIn this episode of Learning Machines 101, we review Chapter 6 of my book “Statistical Machine Learning” which introduces methods for analyzing the behavior of machine inference algorithms and machine learning algorithms as dynamical systems. We show that when dynamical systems can be viewed as special types of optimization algorithms, the behavior of those systems even when they are highly nonlinear and high-dimensional can be analyzed. Learn more by visiting: www.learningmachines101.com and www.statisticalmachinelearning.com .…
L
Learning Machines 101
1 LM101-083: Ch5: How to Use Calculus to Design Learning Machines 34:22
34:22 Play Later Play Later Lists Like Liked34:22
Play Later Play Later Lists Like LikedThis particular podcast covers the material from Chapter 5 of my new book “Statistical Machine Learning: A unified framework” which is now available! The book chapter shows how matrix calculus is very useful for the analysis and design of both linear and nonlinear learning machines with lots of examples. We discuss how to use the matrix chain rule for deriving deep learning descent algorithms and how it is relevant to software implementations of deep learning algorithms. We also discuss how matrix Taylor series expansions are relevant to machine learning algorithm design and the analysis of generalization performance!! For additional details check out: www.learningmachines101.com and www.statisticalmachinelearning.com…
L
Learning Machines 101
1 LM101-082: Ch4: How to Analyze and Design Linear Machines 29:05
29:05 Play Later Play Later Lists Like Liked29:05
Play Later Play Later Lists Like LikedThe main focus of this particular episode covers the material in Chapter 4 of my new forthcoming book titled “Statistical Machine Learning: A unified framework.” Chapter 4 is titled “Linear Algebra for Machine Learning. Many important and widely used machine learning algorithms may be interpreted as linear machines and this chapter shows how to use linear algebra to analyze and design such machines. In addition, these same techniques are fundamentally important for the development of techniques for the analysis and design of nonlinear machines. This podcast provides a brief overview of Linear Algebra for Machine Learning for the general public as well as information for students and instructors regarding the contents of Chapter 4 of Statistical Machine Learning. For more details, check out: www.statisticalmachinelearning.com…
L
Learning Machines 101
1 LM101-081: Ch3: How to Define Machine Learning (or at Least Try) 37:20
37:20 Play Later Play Later Lists Like Liked37:20
Play Later Play Later Lists Like LikedThis particular podcast covers the material in Chapter 3 of my new book “Statistical Machine Learning: A unified framework” with expected publication date May 2020. In this episode we discuss Chapter 3 of my new book which discusses how to formally define machine learning algorithms. Briefly, a learning machine is viewed as a dynamical system that is minimizing an objective function. In addition, the knowledge structure of the learning machine is interpreted as a preference relation graph which is implicitly specified by the objective function. In addition, this week we include in our book review section a new book titled “The Practioner’s Guide to Graph Data ” by Denise Gosnell and Matthias Broecheler. To find out more information visit the website: www.learningmachines101.com .…
L
Learning Machines 101
1 LM101-080: Ch2: How to Represent Knowledge using Set Theory 31:43
31:43 Play Later Play Later Lists Like Liked31:43
Play Later Play Later Lists Like LikedThis 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 Concept Modeling”.…
L
Learning Machines 101
1 LM101-079: Ch1: How to View Learning as Risk Minimization 26:07
26:07 Play Later Play Later Lists Like Liked26:07
Play Later Play Later Lists Like LikedThis 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 because it provides a framework for not only understanding existing algorithms but also for suggesting new algorithms for specific applications.…
L
Learning Machines 101
1 LM101-078: Ch0: How to Become a Machine Learning Expert 39:18
39:18 Play Later Play Later Lists Like Liked39:18
Play Later Play Later Lists Like LikedThis particular podcast (Episode 78 of Learning Machines 101) 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 learning expert! For more information, check out: www.learningmachines101.com…
L
Learning Machines 101
1 LM101-077: How to Choose the Best Model using BIC 24:15
24:15 Play Later Play Later Lists Like Liked24:15
Play Later Play Later Lists Like LikedIn this 77th episode of www.learningmachines101.com , 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 error. The probability of the training data given the model is called the “marginal likelihood”. Using the marginal likelihood, one can calculate the probability of a model given the training data and then use this analysis to support selecting the most probable model, selecting a model that minimizes expected risk, and support Bayesian model averaging. The assumptions which are required for BIC to be a valid approximation for the probability of the training data given the probability model are also discussed.…
L
Learning Machines 101
1 LM101-076: How to Choose the Best Model using AIC and GAIC 28:17
28:17 Play Later Play Later Lists Like Liked28:17
Play Later Play Later Lists Like LikedIn 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 GAIC to be valid, and explicit formulas are provided for the AIC and GAIC so they can be used in practice. Briefly, AIC and GAIC provide a way of estimating the average prediction error of your learning machine on test data without using test data or cross-validation methods. The GAIC is also called the Takeuchi Information Criterion (TIC).…
L
Learning Machines 101
1 LM101-075: Can computers think? A Mathematician's Response (remix) 36:26
36:26 Play Later Play Later Lists Like Liked36:26
Play Later Play Later Lists Like LikedIn 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 brains and other non-standard computing machines. This episode is dedicated to the memory of my mom, Sandy Golden. To learn more about Turing Machines, SuperTuring Machines, Hypercomputation, and my Mom, check out: www.learningmachines101.com…
L
Learning Machines 101
1 LM101-074: How to Represent Knowledge using Logical Rules (remix) 19:22
19:22 Play Later Play Later Lists Like Liked19:22
Play Later Play Later Lists Like LikedIn 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, these challenges include: issues of feature representation, having an adequate number of rules, obtaining rules that are not inconsistent, and having rules that handle special cases and situations. To learn more, visit: www.learningmachines101.com…
L
Learning Machines 101
1 LM101-073: How to Build a Machine that Learns to Play Checkers (remix) 24:58
24:58 Play Later Play Later Lists Like Liked24:58
Play Later Play Later Lists Like LikedThis 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 methods and artificial neural network learning algorithms. The podcast ends with a book review of Professor Nilsson’s book: “The Quest for Artificial Intelligence: A History of Ideas and Achievements” . For more information, check out: www.learningmachines101.com…
L
Learning Machines 101
1 LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002) 22:07
22:07 Play Later Play Later Lists Like Liked22:07
Play Later Play Later Lists Like LikedThis 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 learning and artificial intelligence is discussed and the concept of the Big Artificial Intelligence Magic Show is introduced. At the end of the podcast, the book After Digital: Computation as Done by Brains and Machines by Professor James A. Anderson is briefly reviewed. For more information, please visit: www.learningmachines101.com…
Welcome to Player FM!
Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.
Similar to Learning Machines 101
Android Backstage, a podcast by and for Android developers. Hosted by developers from the Android engineering team, this show covers topics of interest to Android programmers, with in-depth discussions and interviews with engineers on the Android team at Google. Subscribe to Android Developers YouTube → https://goo.gle/AndroidDevs
…
continue reading
The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
…
continue reading
BBC Radio 5 live’s award winning gaming podcast, discussing the world of video games and games culture.
…
continue reading
Africa-focused technology, digital and innovation ecosystem insight and commentary.
…
continue reading
Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
…
continue reading
Show notes are at https://stevelitchfield.com/sshow/chat.html
…
continue reading
Hanselminutes is Fresh Air for Developers. A weekly commute-time podcast that promotes fresh technology and fresh voices. Talk and Tech for Developers, Life-long Learners, and Technologists.
…
continue reading
Why do only 10% of companies succeed with AI? In this series by MIT SMR and BCG, we talk to the leaders who've achieved big wins with AI in their companies and learn how they did it. Hear what gets experts from companies like NASA, Github, and others excited to do their jobs every day and what they consider the keys to their success.
…
continue reading
The power of Data is undeniable. And unharnessed - it’s nothing but chaos. Making data your ally. Using it to lead with confidence and clarity. Host Jess Carter is solving problems in real-time to reveal what’s possible. Helping communities and people thrive. This is Data Driven Leadership, a show brought to you by Resultant.
…
continue reading
Material is a weekly discussion about the Google and Android universe. Your intrepid hosts try to answer the question, “What holds up the digital world?” The answer, so far, is that it’s Google all the way down. Hosted by Andy Ihnatko and Florence Ion.
…
continue reading
Player FM - Podcast App
Go offline with the Player FM app!
Go offline with the Player FM app!
