Instructor: Dr. Isabel K. Darcy,
Department of Mathematics, AMCS, and Informatics,
University of Iowa
Office: B1H MLH
Email: isabel-darcy AT uiowa.edu
Office hours: Tuesdays 8:50 - 9:15am and 12:30 - 2:00pm+, Thursday 8:50 - 9:15am, 12:30 - 12:40pm+and by appointment (Note + means I will normally be available for longer).
TA: Wako Bungula
Office: 325L MLH
Office hours: Tuesdays/Thursday 9:30 - 11:00am and by appointment.
TDA Mapper was developed by Gurjeet Singh, Facundo Memoli and Gunnar Carlsson. The company Ayasdi is based on the Mapper algorithm. Both python versions and R versions are freely available. The algorithm is very simple (bin data into overlapping bins, cluster each bin, create a graph where vertices = clusters and two clusters are connected by an edge if they have points in common).
TENTATIVE CLASS SCHEDULE-ALL DATES SUBJECT TO CHANGE (click on date/section for pdf file of corresponding class material):
During the first 8 weeks we will introduce the TDA mapper algorithm including all needed background (e.g, clustering, PCA, some graph theory, etc.). On most Thursdays we will meet in the B5 MLH computer lab to run software related to the previous Tuesday's lecture. Explicit directions will be provided. The TA and I will be available to provide assistance. Labs can be incorporated into your project. In weeks 9 - 12, we will compare TDA mapper to other data analysis/visualization software. The remaining weeks will include lectures, mentoring talks and group presentations.
Click here for information regarding all assignments including ICON quizzes, HW, presentations, and project.
|1/16||Professor Gunnar Carlsson Introduces Topological Data Analysis, Mapper slides||
Quiz 1 (Due 1/18 at 7am) over TDA Mapper videos:|
HW 2 (Due 1/18) --5 points
|1/18||Meet in B5 MLH: Lab 1 files , Goals |
Start a draft of a poster introducing the TDA mapper algorithm.
|Extracting insights from the shape of complex data using topology P. Y. Lum, G. Singh, A. Lehman, T. Ishkanov, M. Vejdemo-Johansson, M. Alagappan, J. Carlsson, G. Carlsson (2013) video, pptx, pdf|
|1/23||R/Rstudio, More TDA mapper: Filter functions I, Clustering I||
HW 3 (Due 1/23) -- 5 points : |
Draft of a poster introducing the TDA mapper algorithm. (note poster can be printed on normal letter size paper -- make sure you use a readable font size)
|1/25|| Meet in B5 MLH:
Lab 2 goals ,
Lab 2 files
|FYI: scikit-learn clustering|
|1/30||Filter functions II , Clustering II||
HW 5 (Due 2/5) -- 5 points : |
Poster introducing the TDA mapper algorithm (note poster can be printed on normal letter size paper -- make sure you use a readable font size).
|2/1|| Meet in B5 MLH: Lab 3 |
|2/6||Jupyter, Simplicial complexes, etc. , Distances||
Icon Quiz 2 Review (10 points; Due 2/6 at 7:00 AM)|
Project (Due 2/6)
Intro draft including 2, 3, 7-10
|2/8||Meet in B5 MLH: Lab 4 , Jupyter lab files|
|2/13||coloring, KS statistics||Project (Due 2/13) Intro draft including 2, 3, 7-10|
|2/15||Meet in B5 MLH: Lab 5 files , goals|
|2/20||KS statistics, Distances, Talk advice, Github||
Project (Due 2/22) Writing fellow version|
|2/22||Meet in B5 MLH|
|2/27||TDA mapper examples||
Mini-presentation slides first draft due Monday 2/26 at 10am (10 - 20 points)
Mini-presentation slides final draft due Wednesday 2/28 at 10am (10 - 0 points)
HW 6 (Due 3/6) -- 10 points : Practice exam
Icon Quiz 3 Review (25 points; Due 3/5)
Project (due 3/9) Revision of 2/22 version based on writing fellow comments -- extension may be requested depending on suggested revisions.
|3/8||Midterm (50 points)|
|3/20||Tableau; Clustering||Icon Quiz 4 (Due Thursday 3/22 at 7:00 AM) over Voronoi (6:06 min) and k-means (9:10 min)|
|3/22||LAB: meet in B5 MLH (basement computer lab)|
|3/27||Clustering; PCA|| Project (Due Tuesday, 3/27)|
Polished draft including 2, 3 4, 7 - 10.
|3/29||LAB: meet in B5 MLH (basement computer lab)|
Project (due 4/3) |
Draft of your project which should be at least 80% done.
|4/5||LAB: meet in B5 MLH (basement computer lab)|
|4/10||MDS; other methods||Icon Quiz 5 Review (9 points; Due 4/10 at 7:00 AM)
Polished Project (due 4/12): Do not include unfinished sections.
|4/12||Meet in B5 MLH: Lab files ,|
|4/17||other methods; ethics|| Project (due 4/17)
Final project including unfinished sections.
Final presentation slides first draft (due 4/19) -- 15 pts,
|4/19|| Industry Guest Lecturer
|4/24||Guest Lecturer Wako Bungulo: Grad School/Mapper||
Icon Quiz 6 Review (9 points; Due 4/24)|
Final presentation slides 2nd draft (due 4/26, print 6 slides/page and bring to class) -- 15 pts
Finished Project due 4/28.
|5/1|| Student presentations
||Final presentation slides final version (due 4/30 noon) -- 10 pts|
Student presentations |
G. Bowman, X. Huang, Y. Yao, J. Sun, G. Carlsson, L. Guibas and V. Pande, ?Structural insight into RNA Hairpin Folding Intermediates?, Journal of American Chemical Society (Communications). Jul 2008
and data, G Carlsson (2009) (Mapper: p. 281 - 289)
Topological methods for exploring
states in biomolecular folding pathways (2009)
Nov 22 video, pptx, pdf
An eQTL biological data visualization challenge and approaches from the visualization community (2011)
Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival M. Nicolau, A. J. Levine, G. Carlsson (2011) video, pptx, pdf
video, pptx, pdf
Web tool: Progression Analysis of Disease - PAD (includes Mapper)
Ayasdi Iris, academic trial
MICROARRAY VIRTUAL LAB,
How to Analyze DNA Microarray Data, Howard Hughes Medical Institute
Pearson Product-Moment Correlation
Generating and exploring a collection of topological landscapes for visualization of scalar-valued functions. by W. Harvey and Y. Wang, Comput. Graphics Forum (Special issue from EuroVis) 2010
Topological data analysis of Escherichia coli O157:H7 and non-O157 survival in soils (Sept 2014)
Topological methods reveal high and low functioning neuro-phenotypes within fragile X syndrome (Sept 2014)
Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury (Oct 2015)
A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets (March 2016)
Mathworks Matlab Tutorials
Kaggle data analysis competitions
Data for MATLAB hackers (pre-2010)