Learning Machines 101

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

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. In particular, the episode introduces fundamental machine learning concepts such as: probability models, model misspecification, maximum likelihood estimation, and MAP estimation. Check us out at: www.learningmachines101.com

Welcome to the 54th Episode of Learning Machines 101 titled "How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis" (rerun of Episode 40). The principles in this episode are also applicable to the problem of "Market Basket Analysis" and the design of Recommender Systems.

Check it out at: www.learningmachines101.com

and follow us on twitter: @lm101talk

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 yet there is no master control center providing directions to all of the individuals in the group. The global group behavior is an "emergent property" of local interactions among individuals in the group! We will analyze the concept of swarm intelligence as a Markov Random Field, discuss how it can be harnessed to enhance the performance of machine learning algorithms, and comment upon relevant mathematics for analyzing and designing "swarm intelligences" so they behave in an appropriate manner by viewing the Swarm as a nonlinear optimization algorithm. For more information check out: www.learningmachines101.com

and also check us out on twitter (@lm101talk).

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 of the "Kernel Trick" is that you specify similarity relationships among input patterns rather than a recoding transformation to solve a nonlinear problem with a linear learning machine. It's a great magic trick...check it out at: www.learningmachines101.com

where you can obtain transcripts of this episode and download free machine learning software! Also check out the "Statistical Machine Learning Forum" on Linked In and follow us on Twitter using the twitter handle: @lm101talk

This particular podcast is a RERUN of Episode 20 and describes step by step how to download free software which can be used to make predictions using a feedforward artificial neural network whose hidden units are radial basis functions. This is essentially a nonlinear regression modeling problem. We show the performance of this nonlinear learning machine is substantially better on test data set than the linear learning machine software presented in Episode 13. Basically performance for the linear learning machine was about 13% because the data set was specifically designed to be unlearnable by a linear learning machine, while the performance for the nonlinear machine learning software in this episode is about 70%. Again, I'm a little disappointed that only a few people have downloaded the software and tried things out. You can download windows executable, mac executable, or the MATLAB source code.

It's important to actually experiment with real machine learning software if you want to learn about machine learning!

Check out: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!

Or tweet us at: @lm101talk

In this episode we will explain how to download and use free machine learning software from the website: www.learningmachines101.com. This podcast is concerned with the 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. Also follow us on twitter at: lm101talk

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 learns from its experiences and describe the results of some simulation studies. To learn more, visit:

www.learningmachines101.com

to download the free lunar lander software which illustrates principles of temporal reinforcement learning and nonlinear control theory. You will also have the opportunity to download free software which illustrates how a simple deep learning neural network with one layer of radial basis functions works and a simple linear regression model learning machine. Check it out!!!

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 its experiences. For more information, check out:

www.learningmachines101.com

and visit us a twitter: @lm101talk #machinelearning #statistics

#artificialintelligence

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 pattern vectors called “support vectors” which are important for characterizing the Support Vector Machine decision boundary. The relationship of Support Vector Machine parameter estimation and logistic regression parameter estimation is also discussed.For more information..check us out at: www.learningmachines101.com

also check us out on twitter at: lm101talk