Learning Machines 101

In this episode we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of machine learning algorithms.

For more podcast episodes on the topic of machine learning and free machine learning software, please visit us at: www.learningmachines101.com !!

In this 15th episode of Learning Machines 101, we discuss the problem of how to build a machine that can learn any given pattern of inputs and generate any desired pattern of outputs when it is possible to do so! It is assumed that the input patterns consists of zeros and ones indicating possibly the presence or absence of a feature.

Check out: www.learningmachines101.com to obtain transcripts of this podcast!!!

In this episode, we discuss the problem of how to build a machine that can do anything! Or more specifically, given a set of input patterns to the machine and a set of desired output patterns for those input patterns we would like to build a machine that can generate the specified output pattern for a given input pattern. This problem may be interpreted as an example of solving a supervised learning problem. Checkout the shownotes at: www.learningmachines101.com for a transcript of this show and free machine learning software!

Hello everyone! Welcome to the thirteenth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss important concepts of artificial intelligence and machine learning in hopefully an entertaining and educational manner.

In this episode we will explain how to download and use free machine learning software which can be downloaded from the website: www.learningmachines101.com. Although we will continue to focus on critical theoretical concepts in machine learning in future episodes, it is always useful to actually experience how these concepts work in practice. For these reasons, from time to time I will include special podcasts like this one which focus on very practical issues associated with downloading and installing machine learning software on your computer. If you follow these instructions, by the end of this episode you will have installed one of the simplest (yet most widely used) machine learning algorithms on your computer. You can then use the software to make virtually any kind of prediction you like. However, some of these predictions will be good predictions, while other predictions will be poor predictions. For this reason, following the discussion in Episode 12 which was concerned with the problem of evaluating generalization performance, we will also discuss how to evaluate what your learning machine has “memorized” and additionally evaluate the ability of your learning machine to “generalize” and make predictions about things that it has never seen before.

In this episode we discuss the problem of how to evaluate the ability of a learning machine to make generalizations and construct abstractions given the learning machine is provided a finite limited collection of experiences.

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 logical inference.

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

Episode Summary: Today we address a strange yet fundamentally important question. How do you predict the probability of something you have never seen? Or, in other words, how can we accurately estimate the probability of rare events? Show Notes: Hello everyone! Welcome to the eleventh podcast in the podcast series Learning Machines 101. In this series of podcasts. Read More »

The post LM101-011: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws) appeared first on Learning Machines 101.

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

Episode Summary: In this podcast episode, 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 the podcast series Learning Machines 101. In this series of podcasts my goal. Read More »

The post LM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation) appeared first on Learning Machines 101.

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

Episode Summary: Embodied cognition emphasizes the design of complex artificially intelligent systems may be both vastly simplified and vastly enhanced if we view the robotic bodies of artificially intelligent systems as important contributors to intelligent behavior. Show Notes: Hello everyone! Welcome to the ninth podcast in the podcast series Learning Machines 101. In this series of podcasts my. Read More »

The post LM101-009: How to Enhance Intelligence with a Robotic Body (Embodied Cognition) appeared first on Learning Machines 101.

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

Episode Summary: In real life, there is no certainty. There are always exceptions. In this episode, two methods are discussed for making inferences in uncertain environments. In fuzzy set theory, a smart machine has certain beliefs about imprecisely defined concepts. In fuzzy measure theory, a smart machine has beliefs about precisely defined concepts but some beliefs are stronger. Read More »

The post LM101-007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory appeared first on Learning Machines 101.