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Modeling

Instructor: Nick Barton, Chris Wojtan

Teaching Assistant: Zuzana Masárová, Marta Dravecka, Jan Humplik

 

 

Description

 

This course will focus on the concept of modelling, essential throughout science. We will first introduce the concept, emphasizing key issues such as distinguishing between good and bad models, how to develop one's own model, the benefits of simplicity vs. complexity, assumptions and their consequences, and why modelling should be done at all. 
The course will focus on case studies from biology, physics, neuroscience, mathematics, and computer science, and will encourage students to work together to find solutions. Recitations will be used to introduce scientific or mathematical background material.


Requirements/Exams

Weekly homework (40% of final grade), class project (40% of final grade), and a final exam (20% of final grade).

 


Credits

6 ECTS

 

Schedule

Date Topic Instructor Slides Related reading  
6th Oct Introduction, project organisation NB/CW      
8th Oct Approximation 1 CW   The art of insight in science and engineering  
13th Oct Approximation 2 CW Visualization slides    
15th Oct Visualization and scaling NB Probability 1 slides    
20th Oct Probability 1 NB Probability 2 slides Evolution book, chapter 28
Otto and Day
 
22nd Oct Probability 2 NB Inference slides Why most published research findings are false  
27th Oct Inference NB   How scientists fool themselves
Blind analysis: Hide results to seek the truth
 
29th Oct Linear models NB Linear models slides    
3rd Nov Linear models/Nonlinear recursions NB      
5th Nov Stability analysis NB Nonlinear recursions slides Quantitative Universality for a Class of Nonlinear Transformations, When two and two do not make four: Nonlinear phenomena in ecology, Simple mathematical models with complicated dynamics  
10th Nov Mid-term presentations NB/CW      
12th Nov Mid-term presentations NB/CW      
17th Nov Differential equations 0 CW Taylor series    
19th Nov Gaia 1 GN      
24th Nov Gaia 2 GN      
26th Nov Differential equations 1 CW      
1st Dec Differential equations 2 CW      
3rd Dec Dynamics 1 CW      
8th Dec Holiday        
10th Dec Dynamics 2 CW      
15th Dec Wolbachia 1 NB Wolbachia intro slides    
17th Dec Wolbachia 2 NB      
5th Jan Holiday        
7th Jan Bjorn BH      
12th Jan Presentations 1 NB/CW      
14th Jan Presentations 2 NB/CW      
19th Jan Calin 1 CG      
21st Jan Calin 2 CG      
26th Jan Revision/recap CW      
28th Jan Exam CW      

Homework

File Due Date Solutions Notes
HW1 19.10.2015 HW1 solutions Notes on distribution of trailing digits
HW2 26.10.2015 HW2 solutions  
HW3 2.11.2015 Recitation code, HW3 solution  
HW4 9.11.2015 HW4 solutions  
HW6 14.12.2015 Recitation code  
HW7 7.1.2016 Recitation code, HW7 solutions  

Recitations

Date Files
15th Oct Problems,data1,data2
24th Nov Karate_table, R solutions to recitation problems
21st Jan Recitation on hysteresis Mathematica gadget

Class project

The class project consists of an interdisciplinary project on a topic of your choice related to modeling. A one-page abstract describing the goals of your project, the data, the main questions and the approaches you plan to take, is due on Monday, October 19. Midterm presentations are on November 10 and 12. Final presentations are on January 12 and 14. A final write-up of the project of maximally 10 pages will also be due at the end of semester.

Useful resources

Books available in the library course reserve:

- Modelling for field biologists and other interesting people (Hanna Kokko)

- A biologist's guide to mathematical modeling in ecology and evolution (Sarah Otto & Troy Day)

- Models in ecology (John Maynard Smith)

- The analysis of biological data (Michael Whitlock & Dolph Schluter)

- The art of insight in science and engineering (Sanjoy Mahajan)

- Sustainable energy - without the hot air (David Mackay) and here is the (pdf)