1. Developing Computational Skills in the Sciences with MATLAB
Lisa
Alain
Benjamin
Daniel
Presenters: Lisa Kempler, Alain Plattner, Benjamin Bratton, Daniel Zysman
Audience: Science educators

2. The Session Will Give You
Strategies for teaching data analysis, modeling, and computation, in domain-focused courses
Resources for teaching computation in Sciences
Teaching activities (with code)
Tools to address common challenges conveying computational skills in the Sciences
with MATLAB
Access to a community of peer educators

3. Session Flow Session goals
Tour of MATLAB Resources for Sciences: SERC site
3 professors’ representative Teaching Activities
Choice of geophone layout in a simple near-surface seismics setting
Alain Plattner, Geophysics
California State University – Fresno
Building Modular Tools for Visualizing Computation
Benjamin Bratton, Biology
Princeton University
Principal Component Analysis
Daniel Zysman, Neuroscience
Massachusetts Institute of Technology
Q&A

4. Teaching Computation in the Sciences with MATLAB Workshop (Oct 2016)
Objectives and process
Participants
Outcomes

5. Participants and Posted Resourceshe

6. Resource Overview Page: Teaching with MATLAB

7. October 2016 Workshop Outcomes: Teaching Computation with MATLAB

8. SERC Site Resources: Teaching Computation with MATLAB
MATLAB page for educators
data_models/toolsheets/MATLAB.html
Workshop resources and outcomes
teaching_computation/index.html
matlab_computation2016/outcomes.html

9. Session Flow Session goals
Tour of MATLAB Resources for Sciences: SERC site
3 professors’ representative Teaching Activities
Choice of geophone layout in a simple near-surface seismics setting
Alain Plattner, Geophysics
California State University – Fresno
Building Modular Tools for Visualizing Computation
Benjamin Bratton, Biology
Princeton University
Principal Component Analysis
Daniel Zysman, Neuroscience
Massachusetts Institute of Technology
Q&A

10. California State University Fresno
Examples using Matlab / Octave for Experimental Design and Data Processing in a Near-surface Applied Geophysics Class
Alain Plattner
California State University Fresno
April 27, 2017

11. Two Matlab / Octave m-file packagesOn
Seism-O: Simulation of applied geophysics seismic data
Used for teaching concepts and experimental design
GPR-O: Processing and visualization of ground penetrating radar data
Used for teaching concepts and effects of data processing

12. Seism-O: Simple Seismic Data Simulation

13. Seism-O: Simple Seismic Data Simulation

14. Seism-O: Simple Seismic Data Simulation

15. Seism-O: Simple Seismic Data Simulation
Show simulated arrival times
Simulated arrival times using Seism-O for 1 m geophone spacing

16. Seism-O: Simple Seismic Data Simulation
Simulated arrival times for 1 m geophone spacing
Simulated waveforms for 1 m geophone spacing

17. Seism-O: Simple Seismic Data Simulation
Simulated waveforms for 2 m geophone spacing
Simulated waveforms for 1 m geophone spacing

18. Seism-O: Simple Seismic Data Simulation
Software and teaching activity on
Simulated waveforms for 2 m geophone spacing
Simulated waveforms for 1 m geophone spacing

19. GPR-O: GPR data processing & visualization

20. GPR-O: GPR data processing & visualization
Instructions and software on
Simple Matlab / Octave scripts
Ground penetrating radar basic data processing
2-D profile and depth-slice plots
Topography
Educational documentation
GPR data available for example from

21. GPR-O: GPR data processing & visualization
Data: Liu et al. (in prep)

22. GPR-O: GPR data processing & visualization
Data: Liu et al. (in prep)

23. GPR-O: GPR data processing & visualization
Data: Liu et al. (in prep)

24. GPR-O: GPR data processing & visualization
Data: Liu et al. (in prep)

25. GPR-O: GPR data processing & visualization
Tutorial with test data:
Raw field data available for example from:

26. Summary
Seism-O and GPR-O: Matlab / Octave scripts for seismic data simulation and ground penetrating radar data processing
Both available from
For near-surface geophysics class or general data simulation, experimental design, data processing class.

27. Building modular tools for visualizing computation

28. Why are computational tools daunting (for biology graduate students) to use?
“The toolbox can’t find a thing and so it gives an error.”
“I don’t know which parameters are important and which ones I’m not supposed to touch.”
“I understood the code you showed us in class, but when I go to do the homework problems, I can’t figure out what to write.”
“I don’t want to use a tool when I don’t entirely understand the math. What if there’s some important assumption that I’m missing?”
“I used these four different packages and one of them gave me the result that I think will make my boss happy, so I must have done something wrong on the other ones.”

29. Modular problem sets help students quickly gain proficiency
Small pieces with displayed outputs
Reusable pieces
Springboard to independence
“The toolbox can’t find a thing and so it gives an error.”
“I don’t know which parameters are important and which ones I’m not supposed to touch.”
“I understood the code you showed us in class, but when I go to do the homework problems, I can’t figure out what to write.”
“I don’t want to use a tool when I don’t entirely understand the math. What if there’s some important assumption that I’m missing?”
“I used these four different packages and one of them gave me the result that I think will make my boss happy, so I must have done something wrong on the other ones.”

30. What do x(t) and y(t) look like?
Dynamical systems
What do x(t) and y(t) look like?
“The toolbox can’t find a thing and so it gives an error.”
“I don’t know which parameters are important and which ones I’m not supposed to touch.”
“I understood the code you showed us in class, but when I go to do the homework problems, I can’t figure out what to write.”
“I don’t want to use a tool when I don’t entirely understand the math. What if there’s some important assumption that I’m missing?”
“I used these four different packages and one of them gave me the result that I think will make my boss happy, so I must have done something wrong on the other ones.”

31. Dynamical systems
“The toolbox can’t find a thing and so it gives an error.”
“I don’t know which parameters are important and which ones I’m not supposed to touch.”
“I understood the code you showed us in class, but when I go to do the homework problems, I can’t figure out what to write.”
“I don’t want to use a tool when I don’t entirely understand the math. What if there’s some important assumption that I’m missing?”
“I used these four different packages and one of them gave me the result that I think will make my boss happy, so I must have done something wrong on the other ones.”

32. Dynamical systems
“The toolbox can’t find a thing and so it gives an error.”
“I don’t know which parameters are important and which ones I’m not supposed to touch.”
“I understood the code you showed us in class, but when I go to do the homework problems, I can’t figure out what to write.”
“I don’t want to use a tool when I don’t entirely understand the math. What if there’s some important assumption that I’m missing?”
“I used these four different packages and one of them gave me the result that I think will make my boss happy, so I must have done something wrong on the other ones.”

33. Dynamical systems
“The toolbox can’t find a thing and so it gives an error.”
“I don’t know which parameters are important and which ones I’m not supposed to touch.”
“I understood the code you showed us in class, but when I go to do the homework problems, I can’t figure out what to write.”
“I don’t want to use a tool when I don’t entirely understand the math. What if there’s some important assumption that I’m missing?”
“I used these four different packages and one of them gave me the result that I think will make my boss happy, so I must have done something wrong on the other ones.”

34. Dynamical systems
“The toolbox can’t find a thing and so it gives an error.”
“I don’t know which parameters are important and which ones I’m not supposed to touch.”
“I understood the code you showed us in class, but when I go to do the homework problems, I can’t figure out what to write.”
“I don’t want to use a tool when I don’t entirely understand the math. What if there’s some important assumption that I’m missing?”
“I used these four different packages and one of them gave me the result that I think will make my boss happy, so I must have done something wrong on the other ones.”

35. Specific example: Modeling oscillations of glycolysis in yeast
For multiple versions of the code see lization_SERC201610

36. Benefits for modular problems
Students learn when to take breaks
Fail (and succeed) quickly
Encourages code reuse
“The toolbox can’t find a thing and so it gives an error.”
“I don’t know which parameters are important and which ones I’m not supposed to touch.”
“I understood the code you showed us in class, but when I go to do the homework problems, I can’t figure out what to write.”
“I don’t want to use a tool when I don’t entirely understand the math. What if there’s some important assumption that I’m missing?”
“I used these four different packages and one of them gave me the result that I think will make my boss happy, so I must have done something wrong on the other ones.”

37. A principled way to principal components analysis
Daniel Zysman
April 27, 2017

38. Teaching scientific computing in Brain & Cog sciences
The challenge is to deal with students motivation, preparedness and interest:
How to effectively break the ice.
How to cope with inertia.
We need to make it interesting, relevant and transferable.

39. A two tier approach Tier 1:
Learn the basics of data visualization and modelling via toy examples.
Build it step by step.
Tier 2:
Apply the fundamentals to fun and relevant problems.
Photos: Mandana Sassanfar, Quantitative Methods Workshop, CBMM, MIT.

40. Tier 1: PCA Toy Example
Toy data: Bivariate Gaussian, unknown covariance to students.
Task: Find directions of maximum variance.
Rotate
Project
Reveal
Structure

41. Take home messages: Rotations and Projections
Data variability and its geometry
Matrix duality:
to represent data
to transform data
Challenge activity: make a dataset, ask fellow student to reveal underlying structure.

42. Tier 2: PCA for image classification
Teaching activity:
28 by 28 pixels
8-bit gray scale images
These images live in
a 784 dimensional space
Task: classify digits one
from seven using PCA
Revisit:
rotations
projections
MNIST data set

43. Pairwise pixel intensity relations
There are more than possible pairwise pixel plots!!!
Not clear which one to choose.
Which choice is more informative? Try PCA.

44. The 9 images that capture most variance

45. Projections for classification
The first two PCs capture ~37% of the total variance.
The data clusters nicely in this space (linear separability).

46. Learning outcomes PCA reveals underlying structure in the data.
It aids in visualization and classification tasks.
Provides a chance to address short comes, assumptions and pitfalls.
Emphasize the different role of matrices:
To store data
To transform data

47. Student’s feedback The two tier approach:
Keep students engaged and motivated to learn, while being challenged.
Improves confidence and competency to transfer what they have learned to broader contexts.

48. Join the Community: Teaching Computation in Sciences with MATLAB Community

49. Join the Community: Teaching Computation in Sciences with MATLAB Community

50. Future Community Sessions on Teaching with MATLAB
Earth Educators’ Rendezvous, July 17-21, 2017
Early Bird Registration deadline is May 1st!
Mark your calendars!
Teaching Computation with MATLAB in the Sciences Workshop
October 15-17, 2017
Carleton College, Northfield, MN
Rory McFadden if you are interested in joining the community

51. SERC Resources: Teaching Computation with MATLAB
MATLAB page for educators
toolsheets/MATLAB.html
And researchers, too
Workshop outcomes
Teaching Activities
Pedagogy and computational philosophy
Community