Developing Computational Skills in the Sciences with Matlab Webinar 2017
- 1. Developing Computational Skills in the Sciences with MATLAB Presenters: Lisa Kempler, Alain Plattner, Benjamin Bratton, Daniel Zysman Audience: Science educators Lisa Alain Benjamin Daniel
- 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 http://serc.carleton.edu/NAGTWorkshops/data_models/matlab16/index.html
- 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
- 6. Resource Overview Page: Teaching with MATLAB http://serc.carleton.edu/NAGTWorkshops/data_models/toolsheets/MATLAB.html
- 7. October 2016 Workshop Outcomes: Teaching Computation with MATLAB http://serc.carleton.edu/teaching_computation/index.html
- 8. SERC Site Resources: Teaching Computation with MATLAB MATLAB page for educators • http://serc.carleton.edu/NAGTWorkshops/ data_models/toolsheets/MATLAB.html Workshop resources and outcomes • http://serc.carleton.edu/ teaching_computation/index.html • http://serc.carleton.edu/ 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. 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 https://github.com/NSGeophysics
- 11. Two Matlab / Octave m-file packages 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 On https://github.com/NSGeophysics https://github.com/NSGeophysics
- 12. Seism-O: Simple Seismic Data Simulation https://github.com/NSGeophysics
- 13. https://github.com/NSGeophysics Seism-O: Simple Seismic Data Simulation
- 14. air wave direct wave refracted waveReflected wave V1 V2 V0 = 343 m/s https://github.com/NSGeophysics Seism-O: Simple Seismic Data Simulation
- 15. air wave direct wave refracted waveReflected wave V1 V2 V0 = 343 m/s Show simulated arrival times Simulated arrival times using Seism-O for 1 m geophone spacing https://github.com/NSGeophysics Seism-O: Simple Seismic Data Simulation
- 16. Simulated arrival times for 1 m geophone spacing Simulated waveforms for 1 m geophone spacing https://github.com/NSGeophysics Seism-O: Simple Seismic Data Simulation
- 17. Simulated waveforms for 2 m geophone spacing Simulated waveforms for 1 m geophone spacing https://github.com/NSGeophysics Seism-O: Simple Seismic Data Simulation
- 18. Simulated waveforms for 2 m geophone spacing Simulated waveforms for 1 m geophone spacing Software and teaching activity on https://github.com/NSGeophysics/Seism-O/wiki https://github.com/NSGeophysics Seism-O: Simple Seismic Data Simulation
- 19. GPR-O: GPR data processing & visualization https://github.com/NSGeophysics
- 20. Instructions and software on https://github.com/NSGeophysics/GPR-O/wiki - 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 https://alaska.usgs.gov/portal/project.php?project_id=384 https://github.com/NSGeophysics GPR-O: GPR data processing & visualization
- 21. Data: Liu et al. (in prep) https://github.com/NSGeophysics GPR-O: GPR data processing & visualization
- 22. https://github.com/NSGeophysics GPR-O: GPR data processing & visualization Data: Liu et al. (in prep)
- 23. https://github.com/NSGeophysics GPR-O: GPR data processing & visualization Data: Liu et al. (in prep)
- 24. https://github.com/NSGeophysics Data: Liu et al. (in prep) GPR-O: GPR data processing & visualization
- 25. Tutorial with test data: https://github.com/NSGeophysics/GPR-O/blob/master/doc/GPR-O.pdf Raw field data available for example from: https://alaska.usgs.gov/portal/project.php?project_id=384 https://github.com/NSGeophysics GPR-O: GPR data processing & visualization
- 26. Summary - Seism-O and GPR-O: Matlab / Octave scripts for seismic data simulation and ground penetrating radar data processing - Both available from https://github.com/NSGeophysics - For near-surface geophysics class or general data simulation, experimental design, data processing class. https://github.com/NSGeophysics
- 27. Building modular tools for visualizing computation
- 28. Why are computational tools daunting (for biology graduate students) to use?
- 29. Modular problem sets help students quickly gain proficiency • Small pieces with displayed outputs • Reusable pieces • Springboard to independence
- 30. Dynamical systems What do x(t) and y(t) look like?
- 35. Specific example: Modeling oscillations of glycolysis in yeast For multiple versions of the code see https://github.com/bpbratton/buildingModularVisua lization_SERC201610
- 36. Benefits for modular problems •Students learn when to take breaks •Fail (and succeed) quickly •Encourages code reuse
- 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. -8 -6 -4 -2 2 4 6 8 10 X1 -6 -4 -2 2 4 6 X2 -8 -6 -4 -2 2 4 6 8 10 X1 -6 -4 -2 2 4 6 X2 -8 -6 -4 -2 2 4 6 8 10 X1 -6 -4 -2 2 4 6 X2 -8 -6 -4 -2 2 4 6 8 10 X1 -6 -4 -2 2 4 6 X2 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 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 Teaching activity: http://serc.carleton.edu/matlab_computation2016/activities/159581.html MNIST data set http://yann.lecun.com/exdb/mnist/
- 43. Pairwise pixel intensity relations • There are more than 300000 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 • http://serc.carleton.edu/matlab_computatio n2016/community.html
- 49. Join the Community: Teaching Computation in Sciences with MATLAB Community• http://serc.carleton.edu/matlab_computatio n2016/community.html
- 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 • Email Rory McFadden (rmcfadden@carleton.edu) if you are interested in joining the community
- 51. SERC Resources: Teaching Computation with MATLAB MATLAB page for educators • http://serc.carleton.edu/NAGTWorkshops/data_models/ toolsheets/MATLAB.html • And researchers, too Workshop outcomes • Teaching Activities – http://serc.carleton.edu/matlab_computation2016/activities.html • Pedagogy and computational philosophy – http://serc.carleton.edu/teaching_computation/index.html • Community – http://serc.carleton.edu/matlab_computation2016/community.html
Editor's Notes
- #29: “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: “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: “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: “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: “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: “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: “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: “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.”