New England Complex Systems Institute
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Winter Session 2020

Gain new insights that reframe your thinking, specific tools to advance current projects, and perspectives to set new directions.

Dates: January 6 - 17

Location: MIT, Cambridge, MA

 

The NECSI Winter School offers two intensive week-long courses on complexity science: modeling and networks, and data analytics. You may register for any of the weeks. If desired, arrangements for credit at a home institution may be made in advance.

Week 1: January 6-10 CX201: Complex Physical, Biological and Social Systems

Week 2: January 12-17 CX202: Complex Systems Modeling and Networks

Group Projects

Group projects are one of the most rewarding parts of the winter and summer courses. Participants split into project teams and put together a publication quality research project using complex systems tools learned during the week. On the final day of each week, groups present their projects. We consistently receive positive feedback about the projects.

Credit

Arrangements to receive credit for NECSI courses at a home institution should be made in advance. To do so, contact us at programs@necsi.edu.

Schedule

Monday – Thursday, Jan. 6-9: 
Lecture 9 AM – 5 PM 
Group Projects 6 – 8 PM

Friday, Jan. 10: 
Group Presentation 9 AM – 12 PM 
Evaluations and Class Photo 12 – 12:15 PM 
Exam (required if taking course for credit, optional otherwise) 12:30 – 1:30 PM

Sunday, Jan. 12: 
Lab 9 AM – 5 PM

Monday – Thursday, Jan. 13-16: 
Lecture 9 AM – 5 PM 
Group Projects 6 – 8 PM

Friday, Jan. 17: 
Group Presentation 9 AM – 12 PM 
Evaluations and Class Photo 12 – 12:15 PM 
Exam (required if taking course for credit, optional otherwise) 12:30 – 1:30 PM


Overview | Week 1 | Week 2 | Student Reviews | Register

CX201: Complex Physical, Biological & Social Systems

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January 6-10

This course provides an introduction to (a) essential concepts of complex systems and related mathematical methods and simulation strategies with application to physical, biological and social systems, and (b) the fundamentals of data analytics, machine learning, and artificial intelligence.

Concepts to be discussed include: emergence, complexity, networks, self-organization, pattern formation, evolution, adaptation, fractals, chaos, cooperation, competition, attractors, interdependence, scaling, dynamic response, information and function.

Methods to be discussed include: statistical methods, cellular automata, agent-based modeling, pattern recognition, system representation and informatics.

Demonstration of the application of complex systems methods will be made through studies of:

  • Social systems: education system, health care system, military system

  • Psychosocial systems: patterns of social behavior, mind, creativity

  • Biological systems: physiology, brain, cellular systems, genetic networks

  • Physical systems: meteorology

The data analytics lessons will cover skills needed to transform raw data into visualizations and insight. The course will cover fundamental construction and analysis of models including identifying what is to be modeled, constructing a mathematical representation, analysis tools and implementing and simulating the model in a computer program.


Overview | Week 1 | Week 2 | Student Reviews | Register

CX202: Complex Systems Modeling and Networks

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Lab January 12, Course June 13-17

CX102 (Lab): Computer Programming for Complex Systems

This one day lab introduces computer programming in the Python language for those with little or no computer programming experience. It is designed as a precursor to CX202.

The lab will present programming concepts and hands-on exercises. Topics to be covered include: data structures, algorithms, variables and assignments, numerical and logical operations, lists and dictionaries, user-defined functions, flow control, loops, and visualization.

CX202: Complex Systems Modeling and Networks

This course provides an introduction to building models of complex systems (physical, biological, social and engineering), and network architectures dynamic processes.

It will cover the basic construction and analysis of models including identifying what is to be modeled, constructing a mathematical representation, analysis tools and implementing and simulating the model in a computer simulation. Particular attention will be paid to choosing the right level of detail for the model, testing its robustness, and discussing which questions a given model can or cannot answer. Python will be used as a primary computer programming language for modeling and simulation. Prior computer programming experience is helpful, but not required. Students are encouraged to bring to class their own laptop computers.

The study of networks will introduce the use of network topologies and the characterization of networks describing complex systems, including such concepts as small worlds, degree distribution, diameter, clustering coefficient, modules, motifs. Different types of network topologies and network behaviors that model aspects of real complex systems will be described including: modular, sparse, random, scale-free, influence, transport, transformation, and structure.


Overview | Week 1 | Week 2 | Student Reviews | Register

Reviews from Previous NECSI Course Students

 
 
 

"Excellent course...useful thematic overview... applications in diverse contexts were exciting. Particularly appreciated the group project - excellent experiential pedagogy."

"The course was an eye-opening framework to analyze my work through a different lens."

"Presentations were extremely useful for me in understanding how to begin modeling complex systems and assessing them. Helped me understand a lot of things I have been doing so far without clearly understanding the principles."

"This class very much stretched my mind to apply the ideas of complexity to the world... I believe I learned more on a grander scale... will help enrich my vocabulary and the way of thinking in the world with respect to complexity."

"Excellent class. I hope to take a more active role in the community."

"This course contained more insight than any other 'complexity' themed course that I have taken."

 
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