Mathematics for Complex Systems

Melanie Mitchell et al., Complexity Explorer

This course covers several mathematical techniques that are frequently used in complex systems science.   The techniques are covered in independent units, taught by different instructors.  Each unit has its own prerequisites.  Note that this course is meant to introduce students to various important techniques and to provide illustrations of their application in complex systems.  A given unit is not meant to offer complete coverage of its topic or substitute for an entire course on that topic.   

The course includes units designed for beginning math students (those who have completed at least one year of high-school algebra but not calculus) and for more advanced math students (see individual units for prerequisites).    These units are listed below, along with the tentative dates on which they will be available.  If you are enrolled in this course, you will receive notification by email whenever a new unit is available.  Students can take the entire course or are free to follow any of the units independently. 

Units for beginning math students (prerequisite: at least one year of high-school algebra):

Functions and iteration  (David Feldman:  Excerpts from Dave's Dynamical Systems course)
Introduction to differential equations (David Feldman: Excerpts from Dave's Dynamical Systems course)
Vector and matrix algebra (Anthony Rhodes, February, 2014)
Introduction to logarithms (Melanie Mitchell; March, 2014)
Basic probability,  combinatorics, and statistical distributions (TBD)
Introduction to information theory (Jim Crutchfield; March, 2014)

Units for more advanced math students:

Ordinary differential equations (Liz Bradley: Excerpts from Liz's Nonlinear Dynamics course; Prerequisite: Calculus)
Maximum entropy methods (Simon DeDeo; Prerequisite: Calculus, Basic Probability; February, 2014)
Bayesian inference and Bayesian networks (Melanie Mitchell; Prerequisite:  Basic probability; April, 2015)
Introduction to stochastic processes (Prerequisites: Calculus, basic probability; 2015 )
Critical phenomena (Prerequisite: Calculus, basic probability;  2015)
Renormalization methods (Prerequisite: Calculus;  2015)



  • 29 сентября 2014
Характеристики онлайн курса:
  • Бесплатный:
  • Платный:
  • Сертификат:
  • MOOC:
  • Видеолекции:
  • Аудиолекции:
  • Email-курс:
  • Язык: Английский Gb


Пока никто не написал отзыв по этому курсу. Хотите быть первым?

Зарегистрируйтесь, чтобы оставить отзыв

Входит в подборки курсов:
Slide1 Complexity
Complexity theory and Modeling
Introcomplexitylogo Network Science
Networks, graphs etc.
Ещё курсы на эту тему:
2-882s05 System Design and Analysis based on AD and Complexity Theories
This course studies what makes a good design and how one develops a good design...
Esd-77s10 Multidisciplinary System Design Optimization
There is need for a rigorous, quantitative multidisciplinary design methodology...
Esd-04js07 Frameworks and Models in Engineering Systems / Engineering System Design
This class provides an introduction to quantitative models and qualitative frameworks...
Esd-342s10 Network Representations of Complex Engineering Systems
This course provides a deep understanding of engineering systems at a level...
Esd-342s06 Advanced System Architecture
This course provides a deep understanding of engineering systems at a level...
Ещё из рубрики «Математика и статистика»:
C2750912-8e29-426f-91b8-c03b0dd9ee8f-d3ce8d3f0f02.small Autonomous Mobile Robots
Basic concepts and algorithms for locomotion, perception, and intelligent navigation...
C9d14131-a515-462b-82e2-6eaec5bc1c17-07a3a07fc0a1.small Using Python for Research
Take your introductory knowledge of Python programming to the next level and...
D47ddc85-0bf0-41d9-94ff-4fde53a95d1a-d7b491c99677.small Deep Learning Fundamentals with Keras
New to deep learning? Start with this course, that will not only introduce you...
50101caa-3572-4dd7-aa84-01f1d53c8042-e2e6f25d8354.small Deep Learning with Tensorflow
Much of theworld's data is unstructured. Think images, sound, and textual data...
A7660cab-402b-41f5-9a4e-cd5dd1badcd0-7cf26b5a552c.small Using GPUs to Scale and Speed-up Deep Learning
Note: The program that this course is a part of is changing and this course...
Ещё от Complexity Explorer:
Butterflies Agent-Based Modeling in NetLogo
Agent-Based Modeling in NetLogo
Introcomplexitylogo Introduction to Complexity (Fall, 2013)
In this course you'll learn about the tools used by scientists to understand...
Bifurcationdiagram Introduction to Dynamical Systems and Chaos
Introduction to Dynamical Systems and Chaos
Logo_complexity Nonlinear Dynamics (with calculus)
Nonlinear Dynamics (with calculus)
Lorenzattractor_view Nonlinear Dynamics: Mathematical and Computational Approaches
This course provides a broad introduction to the field of nonlinear dynamics...

© 2013-2019