This course continues our data structures and algorithms specialization by focussing on the use of linear and integer programming formulations for solving algorithmic problems that seek optimal ...

Now that you've got a good sense of how to 'speak' R, let's use it with linear regression to make distinctive predictions.

In regression problems alternative criteria of "best fit" to least squares are least absolute deviations and least maximum deviations. In this paper it is noted that linear programming techniques may ...

Before building my model, I want to step back to offer an easy-to-understand definition of linear regression and why it’s vital to analyzing data. What is linear regression?

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

Stable Matchings, Optimal Assignments, and Linear Programming By: A. E. Roth, U. G. Rothblum and J. H. Vande Vate ...

A solid coverage of the most important parts of the theory and application of regression models, and generalised linear models. Multiple regression and regression diagnostics. Generalised linear ...

An example of the quadratic assignment problem (QAP) is the facility location problem, in which n facilities are assigned, at minimum cost, to n sites. Between each pair of facilities, there is a ...

Genetic programming (GP) represents a class of evolutionary algorithms that automates the creation of computer programmes to solve complex problems. Coupled with symbolic regression (SR), which ...