Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life. For example, artificially intelligent systems recognize our voices, sort our pictures, make purchasing suggestions, and can automatically fly planes and drive cars. In this podcast series, we examine such questions such as: How do these devices work? Where do they come from? And how can we make them even smarter and more human-like? These are the questions which will be addressed in the podcast series Learning Machines 101.
In this episode rerun 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. Check out the website:
www.learningmachines101.com
to obtain a transcript of this episode!
In this rerun of episode 24 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 leads us to the topic of stochastic model search and evaluation. Check out the blog with additional technical references at: www.learningmachines101.com
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 whose behavior evolves as the learning machines become increasingly smarter. The essential idea for the construction of such reinforcement learning machines is based upon first developing a supervised learning machine. The supervised learning machine then “guesses” the desired response and updates its parameters using its guess for the desired response! Although the reasoning seems circular, this approach in fact is a variation of the important widely used machine learning method of Expectation-Maximization. Some applications to learning to play video games, control walking robots, and developing optimal trading strategies for the stock market are briefly mentioned as well. Check us out at: www.learningmachines101.com
This 62nd episode of Learning Machines 101 (www.learningmachines101.com) 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 an episode when only the beginning of the episode is observable. This estimated Value Function can then be used by the learning machine to select a particular action in a given situation to minimize the total future penalties that will be received. Applications include: building your own robot, building your own automatic aircraft lander, building your own automated stock market trading system, and building your own self-driving car!!
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 topics associated with the Introduction to Reinforcement Learning with Function Approximation Tutorial presented by Professor Richard Sutton on the first day of the conference.
This episode is a RERUN of an episode originally presented in January 2016 and lays the groundwork for future episodes on the topic of reinforcement learning!
Check out: www.learningmachines101.com for more info!!
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 talk by Chief Data Scientist Ira Cohen of Anodot presented at the 2016 Berlin Buzzwords Data Science Conference. Check out: www.learningmachines101.com to hear the podcast or read a transcription of the podcast!
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. For more details visit us at: www.learningmachines101.com
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 provides a unified theoretical framework for the development of a wide range of methods for determining if a learning machine is capable of learning its statistical environment. The article is co-authored by myself, Steven Henley, Halbert White, and Michael Kashner. It is an open-access article so the complete article can be downloaded for free! The download link can be found in the show notes of this episode at: www.learningmachines101.com . In 30 years everyone will be using these methods so you might as well start using them now!
In this 57th episode, we explain how to use unsupervised machine learning algorithms to catch internet criminals who try to steal your money electronically! Check it out at: www.learningmachines101.com
In this NEW 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 systems, search engines, and recommender systems. Check us out at: www.learningmachines101.com
and follow us on twitter at: @lm101talk
