The course gives an introduction to discrete mathematical techniques and their applications.
More info:http://www.uninettunouniversity.net/en/mooc-program.aspx?lf=en&courseid=3004&degree=140&planid=146&faculty=0
The course gives an introduction to discrete mathematical techniques and their applications.
More info:http://www.uninettunouniversity.net/en/mooc-program.aspx?lf=en&courseid=3004&degree=140&planid=146&faculty=0
Learn how to think the way mathematicians do - a powerful cognitive process developed over thousands of years. Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself.
The course gives an introduction to discrete mathematical techniques and their applications.
At the completion of this course, the student should be able to: demonstrate knowledge and understanding of the fundamentals of information theory; appreciate the notion of fundamental limits in communication systems and more generally all systems; develop deeper understanding of communication systems; apply the concepts of information theory to various disciplines in information science.
Il corso di Complementi di Matematica è il completamento dei due corsi di carattere analitico-matematico e geometrico-algebrico, svolti nel primo anno del corso di studi.
Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will teach you the most fundamental Linear Algebra that you will need for a career in Data Science without a ton of unnecessary proofs and concepts that you may never use. Consider this an expressway to Data Science with approachable methods and friendly concepts that will guide you to truly understanding the most important ideas in Linear Algebra.
¿Por qué las grúas no se vuelcan al levantar grandes pesos? ¿por qué las mesas sólo necesitan tres patas (no alineadas) para apoyarse? Este curso te guiará paso a paso en los conocimientos básicos para entender el equilibrio de estructuras simples.
Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.
This course is an introduction to the finite element method as applicable to a range of problems in physics and engineering sciences. The treatment is mathematical, but only for the purpose of clarifying the formulation. The emphasis is on coding up the formulations in a modern, open-source environment that can be expanded to other applications, subsequently.
We invite you to a fascinating journey into Graph Theory — an area which connects the elegance of painting and the rigor of mathematics; is simple, but not unsophisticated. Graph Theory gives us, both an easy way to pictorially represent many major mathematical results, and insights into the deep theories behind them. In this course, among other intriguing applications, we will see how GPS systems find shortest routes, how engineers design integrated circuits, how biologists assemble genomes, why a political map can always be colored using a few colors. We will study Ramsey Theory which proves that in a large system, complete disorder is impossible!
Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction.
This course is about differential equations and covers material that all engineers should know. Both basic theory and applications are taught. In the first five weeks we will learn about ordinary differential equations, and in the final week, partial differential equations. The course is composed of 56 short lecture videos, with a few simple problems to solve following each lecture. And after each substantial topic, there is a short practice quiz. Solutions to the problems and practice quizzes can be found in instructor-provided lecture notes. There are a total of six weeks in the course, and at the end of each week there is an assessed quiz.
The course offers undergraduate students a rather broad view on Automatic Control methodologies and techniques for feedback linear systems.