Vector spaces, matrices and linear applications.
More info:http://www.uninettunouniversity.net/en/mooc-program.aspx?lf=en&courseid=3565&degree=140&planid=146&faculty=0
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En este curso de acceso gratuito*, conocerás algunos temas de las matemáticas escolares con la profundidad necesaria para que puedas ayudar a tus estudiantes a aprenderlas. En este curso, podrás conocer las matemáticas desde cuatro perspectivas: su historia, los conceptos y procedimientos que las caracterizan, las distintas formas en que se hacen presentes (p. ej., tablas, gráficas o expresiones simbólicas), y los fenómenos y situaciones que les dan sentido.
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En este tercer curso de acceso gratuito* del programa especializado Educación Matemática para profesores de primaria, conocerás los conceptos y técnicas para planificar e implementar tus clases. El curso tiene una duración aproximada de seis semanas, con una dedicación promedio de 4 horas semanales. Todas las evaluaciones tienen retroalimentación y podrás descargar la mayoría de los recursos del curso.
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The course offers undergraduate students a rather broad view on Automatic Control methodologies and techniques for feedback linear systems.
UNINETTUNO OpenupEd
The course provides an introduction to the mathematical analysis and linear algebra. The course starts with the real numbers and the related one-variable real functions by studying limits, and continuity.
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This course is all about data and how it is critical to the success of your applied machine learning model.
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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.
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En este segundo curso de acceso gratuito* del programa especializado Educación Matemática para profesores de primaria, conocerás las cuestiones particulares sobre el aprendizaje de las matemáticas y las dificultades y los errores más frecuentes que tienen que enfrentar los estudiantes al aprenderlas. El curso tiene una duración aproximada de seis semanas, con una dedicación promedio de 4 horas semanales. Todas las evaluaciones tienen retroalimentación y podrás descargar la mayoría de los recursos del curso.
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This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning.
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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.
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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.
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This course is the final course in a three part algebra sequence, In this course, students extend their knowledge of more advanced functions, and apply and model them using both algebraic and geometric techniques. This course enables students to make logical deductions and arrive at reasonable conclusions.
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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 Calculus concepts that you will need for a career in Data Science without a ton of unnecessary proofs and techniques that you may never use.