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Archive for the ‘Lectures’ Category

One of the coolest biology professors at MIT, Professor David E. Housman, explaining the Poisson distribution. From 7.27 Principles of Disease, Spring 2008. His description from the class website: “I thought you might like to hear a bit more about the Poisson distribution. It really is quite important in biology. I thought one way I could do this would be to make a little quick time video for you on this topic. So I did. Let me know if you are actually able to download it and view it.”

Probability that I picked a fair coin given that I flipped 4 out of 6 heads.

In this tutorial you are shown how to calculate the mean and standard deviation from a normal distribution using the following example. A high jumper knows from experience that she can clear a height of at least 1.78m once in 5 attempts. She also knows that she can clear a height of at least 1.65m on 7 out of 10 attempts. Find to 3 dp the mean and standard deviation of the heights the jumper can reach

Probability density functions for continuous random variables.

In this session, Jennifer Thompson, MS, introduces data cleaning and outliers. Outliers can be a tricky problem for a data mining project. This session will address these problems and help understand what caused them in the first place.

79-80, mean and standard deviation

A simple (two-variable) regression has three standard errors: one for each coefficient (slope, intercept) and one for the predicted Y (standard error of regression). While the population regression function (PRF) is singular, sample regression functions (SRF) are plural. Each sample produces a (slightly?) different SRF. So, the coefficients exhibit dispersion (sampling distribution). The standard error is the measure of this dispersion: it is the standard deviation of the coefficient….

Lecture Series on Probability and Random Variables by Prof. M. Chakraborty, Department of Electronics and Electrical Communication Engineering, IIT,Kharagpur. For more details on NPTEL visit nptel.iitm.ac.in. Lecture Title to the Theory of Probability and Random Process

This is the third sample video from the 10 hour “Probability and Statistics Tutor” DVD available at MathTutorDVD.com. This clip introduces the concept of standard deviation.

The underlying ideas for statistics. A tutorial on different kinds of variation, especially related to sampling.

More on probability.

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Welcome to StatSoft’s Introduction to Data Mining video series. The series covers hands on tutorials of data mining applications. Subscribe to this series at www.statsoft.com/dmsubscribe to be notified as each future episode becomes available and for any supplementary materials provided for a lesson. … “Data Mining” “predictive modeling” “predictive analytics” “data analysis. analytics” “business intelligence” STATISTICA

A tutorial video clip describing model building in Management Science.

We introduce Queueing theory. Impatient customer strategies such as balking, reneging and colonizing are discussed as well as other assumptions in Queueing Theory.

Google Tech Talks March, 25 2008 ABSTRACT SVN Vishwanathan – Research Scientist Regularized risk minimization is at the heart of many machine learning algorithms. The underlying objective function to be minimized is convex, and often non-smooth. Classical optimization algorithms cannot handle this efficiently. In this talk we present two algorithms for dealing with convex non-smooth objective functions. First, we extend the well known BFGS quasi-Newton algorithm to handle non-smooth functions. Second, we show how bundle methods can be applied in a machine learning context. We present both theoretical and experimental justification of our algorithms. Speaker: SVN Vishwanathan – Research Scientist – Zurich SVN Vishwanathan is a principal researcher in the Statistical Machine Learning program, National ICT Australia with an adjunct appointment at the College of Engineering and Computer Science(CECS), Australian National University. I got my Ph.D in 2002 from the Department of Computer Science and Automation (CSA) at the Indian Institute of Science.