"Algorithm" Open Courses - Page 3

University of Minnesota's course introduces students to concepts, applications, algorithms, programming, and design of recommender systems.

This course will cover the simplex algorithm, the duality theory, the Ellipsoid algorithm, and more.

Students in this course are introduced to the many concepts, techniques, and algorithms commonly found in machine learning.

Students in this course will be introduced to modeling methods that can be used in the context articles of system design, followed by a discussion of the main issues that are to be considered when one want to design systems.

This lesson covers how to define your own methods in Ruby, as well as how to use blocks to develop powerful sorting algorithms.

Concepts covered in this course include the artificial neuron model, linear regression, and the gradient descent algorithm.

This course will discuss number theory as it relates to Euclid's algorithm, linear Diophantine, and more.

Students in this course will study algorithms that use numerical approximation for the problems of mathematical analysis (as distinguished from discrete mathematics).

In this course, students explore elements of zero-sum, two-person game theory, combinatorial algorithms, and linear and integer programming.

Learn the methods and algorithms for discrete control, dynamic optimization, and others in this course devoted to optimization.

During this course, students will learn about the algorithms, applications, and theory of optimization in network applications related tobsuiness.

This course investigates random sampling, random selection of witnesses, symmetry breaking, and Markov chains as examples of how randomization may create simpler and more efficient algorithms.

This course covers remote sensing, which is the practice of measuring the amount of electromagnetic energy coming from an object or area and then analyzing it using various equations and algorithms.

Through this course students learn math basics of parallel hardware, including computer arithmetic and algorithmic foundations.

Students in this course will explore topics like convex sets, functions and optimization problems, while also familiarizing themselves with various algorithms.

This class explores empirical process theory and other machine learning algorithms.

This course covers geometric applications in modern algorithm design.