SOCR News MIDAS Biomedical Bootcamp 2022 - Revision history

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2022-07-17T17:25:55Z

‎Program Schedule

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Dinov: /* Program Schedule */

2022-04-22T14:39:05Z

‎Program Schedule

Dinov: /* Additional Resources */

2022-04-22T14:36:52Z

‎Additional Resources

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== [[SOCR_News | SOCR News & Events]]: 2022 MIDAS Data Science for Biomedical Scientists Bootcamp ==

[[Image:MIDAS_Bootcamp_anime4.gif|right| [https://midas.umich.edu/data-science-for-biomedical-scientists/ 2022 MIDAS Biomedical Bootcamp] ]]

The [https://midas.umich.edu/ Michigan Institute for Data Science (MIDAS)] is organizing a week-long Data Science for Biomedical Scientists Bootcamp. This workshop will introduce data science from a biomedical perspective. Bootcamp participants will learn about practical data science applications in biomedical and health case-studies. Modern data science, machine learning, artificial intelligence, and biostatistical methods will be integrated into the training curriculum.

== Instructors ==
: [https://medicine.umich.edu/dept/dcmb/kayvan-najarian-phd Kayvan Najarian]
: [https://medicine.umich.edu/dept/ophthalmology/nambi-nallasamy-md Nambi Nallasamy]
: [https://umich.edu/~dinov Ivo Dinov], [https://www.umich.edu University of Michigan], [https://www.socr.umich.edu SOCR], [https://midas.umich.edu MIDAS].
: [https://medicine.umich.edu/dept/dcmb/michael-mathis-md Michael Mathis]
: [https://ihpi.umich.edu/our-experts/ryanstid Ryan Stidham]
: [https://medicine.umich.edu/dept/dcmb/jonathan-gryak-phd Jonathan Gryak]
: [https://ihpi.umich.edu/our-experts/msjoding Michael Sjoding]

==Workshop Logistics==
* '''Dates/Times''': Monday through Friday, July 18-22, 2022, 7:00-16:00 [https://www.timeanddate.com/time/zones/et US-EDT] (daily).
* '''Registration''': [https://forms.gle/U7FBrmbghfMFPuLr6 Registration Link (TBD)].
* '''URL''': [https://midas.umich.edu/data-science-for-biomedical-scientists/ MIDAS Bootcamp Website].
* '''Session Format''': Two daily sessions (4-hours each).
* [https://wiki.socr.umich.edu/index.php/SOCR_News_MIDAS_Biomedical_Bootcamp_2022 Session URL].

== Overview==

: ''Target Audience'': This workshop is open to all biomedical scientists. The curriculum is geared towards junior faculty members who plan to incorporate data science in their scholarly work.

: ''Prerequisite'': College level math and statistics.

: ''Main components'':
:: Math and algorithmic foundations for data science
:: Key concepts of data science
:: Introduction to Python programming
:: Machine learning, support vector machine, artificial neural network, deep learning
:: Example of biomedical research projects with data science
:: Incorporating data science in biomedical grant proposals

==Program Schedule==

{| class="wikitable"
! Day !! Time !! Instructor !! Session Topic !! Content
|-
| rowspan="18"|Monday || 7:00 - 8:30 AM || Kayvan Najarian || Session 1: Welcome and introduction to the program || A review of the program and logistics
|-
| || || || Why data science, artificial intelligence, and machine learning?
|-
| 8:30 - 8:45 AM || colspan="3"|Break
|-
| 8:45 - 10:15 AM || Ivo Dinov || [https://socr.umich.edu/BPAD/BPAD_notes/Biophysics430_Chap01_MathFoundations.html Session 2: Math foundations I – Brief introduction to mathematical foundations of machine learning] || Math notation and fundamentals
|-
| || || || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/04_LinearAlgebraMatrixComputing.html Linear Algebra and Matrix Computing]
|-
| || || || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/21_FunctionOptimization.html#10_Practice_examples Optimization theory]
|-
| || || || [https://socr.umich.edu/BPAD/BPAD_notes/Biophysics430_Chap01_MathFoundations.html Differential Equations]
|-
| || || || [https://socr.umich.edu/BPAD/BPAD_notes/Biophysics430_Chap01_MathFoundations.html Calculus of Differentiation & Integration]
|-
| 10:15 - 10:30 AM || colspan="3"|Break
|-
| 10:30 AM - 12:00 PM || Ivo Dinov || Session 3: Math foundations II – Brief introduction to mathematical foundations of machine learning || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/05_DimensionalityReduction.html Dimensionality]
|-
| || || || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/05_DimensionalityReduction.html#1_Example:_Reducing_2D_to_1D Principal Component Analysis (PCA)]
|-
| || || || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/05_DimensionalityReduction.html#103_Hand-Written_Digits_Recognition High-dimensional Visualization (hands-on demos)]
|-
| 12:00 - 1:00 PM || Lunch Break || ||
|-
| 1:00 - 2:30 PM || Kayvan Najarian || Session 4: Clustering vs Classification; k-means; k-Nearest Neighbors || Supervised & Unsupervised methods
|-
| || || || k-means/Spectral/Hierarchical clustering (unsupervised)
|-
| || || || k-NN (supervised), Naïve Bayes classification
|-
| 2:30 - 2:45 PM || colspan="3"|Break
|-
| 2:45 - 4:15 PM || TBA || Session 5: Introduction to Python programming || Basics of Python programming
|-
| rowspan="12"|Tuesday || 7:00 - 8:30 AM || Ivo Dinov || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/09_RegressionForecasting.html#21_Simple_linear_regression Session 6: Linear regression, logistic regression] || Simple linear regression, logit modeling
|-
| || || || Ordinary least squares estimation
|-
| || || || Example scenarios
|-
| || || || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/17_RegularizedLinModel_KnockoffFilter.html#10_Knockoff_Filtering_(FDR-Controlled_Feature_Selection) Controlled feature selection (knockoff)]
|-
| 8:30 - 8:45 AM || colspan="3"|Break
|-
| 8:45 - 10:15 AM || Kayvan Najarian || Session 7: Simple classification methods and feature analysis || Naïve Bayes classification, Feature selection and reduction
|-
| 10:15 - 10:30 AM || Break || ||
|-
| 10:30 AM - 12:00 PM || Kayvan Najarian || Session 8: Model validation and assessment || Metrics for assessment of model performance, n-fold cross validation
|-
| 12:00 - 1:00 PM || colspan="3"| Lunch Break
|-
| 1:00 - 2:30 PM || Michael Mathis || Session 9: Using machine learning for clinical and health applications I ||
|-
| 2:30 - 2:45 PM || colspan="3"|Break
|-
| 2:45 - 4:15 PM || TBA || Session 10: Python programming for linear regression, logistic regression; ridge regression and Naïve Bayes || Python for applying simple machine learning methods to a clinical decision-making problem
|-
| rowspan="11"|Wednesday || 7:00 - 8:30 AM || Kayvan Najarian || Session 11: Artificial neural networks I || Fundamentals of artificial neural networks and their advantages/limitations
|-
| || || ||
|-
| 8:30 - 8:45 AM || colspan="3"|Break
|-
| 8:45 - 10:15 AM || Kayvan Najarian || Session 12: Regression trees || Classification and regression tree (CART)
|-
| 10:15 - 10:30 AM || colspan="3"|Break
|-
| 10:30 AM - 12:00 PM || Kayvan Najarian || Session 13: Random Forest || Ensemble use of regression trees for random forest and other boosting methods
|-
| 12:00 - 1:00 PM || colspan="3"|Lunch Break
|-
| 1:00 - 2:30 PM || Ryan Stidham || Session 14: Using machine learning for clinical and health applications II ||
|-
| || || ||
|-
| 2:30 - 2:45 PM || colspan="3"|Break
|-
| 2:45 - 4:15 PM || TBA || Session 15: Python programming for neural networks, regression trees and random forest || Python for applying CART, random forest, and neural networks to a clinical decision-making problem
|-
| rowspan="10"|Thursday || 7:00 - 8:30 AM || Kayvan Najarian || Session 16: Support vector machines || Using Kernel methods for support vector machines (SVM)
|-
| 8:30 - 8:45 AM || colspan="3"|Break
|-
| 8:45 - 10:15 AM || Jonathan Gryak || Session 17: Deep Learning I || Deep Learning overview, appropriate uses of deep learning, convolutional neural networks, U-Net
|-
| 10:15 - 10:30 AM || colspan="3"|Break
|-
| 10:45 AM - 12:00 PM || Jonathan Gryak || Session 18: Deep Learning II || LSTM, Autoencoders
|-
| 12:00 - 1:00 PM || colspan="3"|Lunch Break
|-
| 1:00 - 2:30 PM || TBA || Session 19: Python programming for support vector machine || Python for applying SVM to a clinical decision-making problem
|-
| 2:30 - 2:45 PM || colspan="3"|Break
|-
| 2:45-4:15 PM || TBA || Session 20: Python programming for deep learning || Python for applying deep learning models to a clinical decision-making problem
|-
| 7:00-8:30 AM || || ||
|-
| rowspan="10"|Friday || || Kayvan Najarian || Session 21: Strategies to add a data science flavor to health-related projects and grant proposals || Some general tips on how to integrate data Scientific ideas in primarily clinical/biomedical grant proposals
|-
| 8:30-8:45 AM || colspan="3"|Break
|-
| 8:45 - 10:15 AM || Michael Sjoding || Session 22: Using machine learning for clinical and health applications III ||
|-
| 10:15-10:30 AM || colspan="3"|Break
|-
| 10:30 AM - 12:00 PM || Nambi Nallasamy || Session 23: Using machine learning for clinical and health applications IV ||
|-
| 12:00-1:00 PM || colspan="3"| Lunch Break
|-
| 1:00 - 2:30 PM || Michael Mathis || Session 24: Guidelines on using machine learning for clinical applications ||
|-
| || || ||
|-
| 2:30 - 2:45 PM || colspan="3"|Break
|-
| 2:45 - 4:15 PM || Ivo Dinov, Jonathan Gryak, Michael Mathis, Kayvan Najarian Nambi Nallasamy, and Michael Sjoding || Session 25: Wrap-up || Q&A; plans for follow-up sessions during the coming year
|}

==Capstone Project==
Interactive-learning (open-ended) project using a [https://umich.instructure.com/files/20935287/download?download_frd=1 large Autism data tensor (n=1,098; k=2,145)]. Use the [https://umich.instructure.com/files/20935365/download?download_frd=1 RMD source], the example [https://umich.instructure.com/files/20935366/download?download_frd=1 HTML output], and the [https://umich.instructure.com/files/20935287/download?download_frd=1 provided data] to experiment with some of the [https://www.socr.umich.edu/people/dinov/courses/DSPA_Topics.html DSPA techniques]. Think of ways to augment these data (e.g., expand the time range and increase the feature richness).

==Additional Resources==
* [https://wiki.socr.umich.edu/images/7/7e/Dinov_MathFoundations_2021_MIDAS_Biomed_Bootcamp_July_2021_Notes.pdf Sessions 2, 3, 6 Roadmap/Summary].
* [https://en.wikipedia.org/wiki/Data_Science_and_Predictive_Analytics DSPA Wikipedia].
* [https://www.socr.umich.edu/people/dinov/courses/DSPA_Topics.html DSPA Appendices]: Bayesian Simulation, Modeling and Inference » Information-Theoretic Foundation of Statistical Learning » Surface, Shape, and Manifold Representation and Visualization » Power Analysis in Experimental Design » Database SQL/NoSQL Queries & Google BigQuery » Image Convolution, Filtering, & Fourier Transform » Causality, Transfer Entropy, & Mechanistic Effects » Agent-based Reinforcement Learning.
* [https://www.springer.com/us/book/9783319723464 DSPA Springer Page] & [http://link.springer.com/978-3-319-72347-1 SpringerLink (PDF Download)].
* [https://dspa2.predictive.space/ dspa2.predictive.space] & [https://umich.instructure.com/courses/143011/ DSPA MOOC Canvas Site].

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← Older revision		Revision as of 14:39, 22 April 2022
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	==Program Schedule==		==Program Schedule==
		+
		+	This [https://wiki.socr.umich.edu/images/a/a1/Dinov_MathFoundations_2022_MIDAS_Biomed_Bootcamp_Notes.pdf One-Page PDF summary includes a more compact outline with live links to dynamic content].

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 |  10:30 AM - 12:00 PM || Ivo Dinov || Session 3: Math foundations II – Brief introduction to mathematical foundations of machine learning || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/05_DimensionalityReduction.html Dimensionality]
 |-
-|   ||  ||  || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/05_DimensionalityReduction.html#1_Example:_Reducing_2D_to_1D Principal Component Analysis (PCA)]
+|   ||  ||  || [https://socr.umich.edu/DSPA2/DSPA2_notes/04_DimensionalityReduction.html#1_Motivational_Example:_Reducing_2D_to_1D Principal Component Analysis (PCA)]
 |-
-|   ||  ||  || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/05_DimensionalityReduction.html#103_Hand-Written_Digits_Recognition High-dimensional Visualization (hands-on demos)]
+|   ||  ||  || [https://socr.umich.edu/DSPA2/DSPA2_notes/04_DimensionalityReduction.html#92_t-SNE_Example:_Hand-written_Digit_Recognition High-dimensional Visualization (hands-on demos)]
 |-
 | 12:00 - 1:00 PM || Lunch Break ||  ||
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 |  2:45 - 4:15 PM || TBA || Session 5: Introduction to Python programming || Basics of Python programming
 |-
-| rowspan="12"|Tuesday || 7:00 - 8:30 AM || Ivo Dinov || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/09_RegressionForecasting.html#21_Simple_linear_regression Session 6: Linear regression, logistic regression] || Simple linear regression, logit modeling
+| rowspan="12"|Tuesday || 7:00 - 8:30 AM || Ivo Dinov || [https://socr.umich.edu/DSPA2/DSPA2_notes/03_LinearAlgebraMatrixComputingRegression.html Session 6: Linear regression, logistic regression] || Simple linear regression, logit modeling
 |-
 | ||  ||  || Ordinary least squares estimation
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 |  ||  ||  || Example scenarios
 |-
-|  ||  ||  || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/17_RegularizedLinModel_KnockoffFilter.html#10_Knockoff_Filtering_(FDR-Controlled_Feature_Selection) Controlled feature selection (knockoff)]
+|  ||  ||  || [https://socr.umich.edu/DSPA2/DSPA2_notes/11_FeatureSelection.html Controlled feature selection (knockoff)]
 |-
 |  8:30 - 8:45 AM || colspan="3"|Break

@@ Line 21: / Line 21: @@
 * '''Session Format''':  Two daily sessions (4-hours each).
 * [https://wiki.socr.umich.edu/index.php/SOCR_News_MIDAS_Biomedical_Bootcamp_2022 Session URL].
 == Overview==

@@ Line 170: / Line 170: @@
 ==Additional Resources==
-* [https://wiki.socr.umich.edu/images/7/7e/Dinov_MathFoundations_2021_MIDAS_Biomed_Bootcamp_July_2021_Notes.pdf Sessions 2, 3, 6 Roadmap/Summary].
+* [https://wiki.socr.umich.edu/images/a/a1/Dinov_MathFoundations_2022_MIDAS_Biomed_Bootcamp_Notes.pdf Materials for Ivo Dinov's Sessions - Roadmap/Summary].
 * [https://en.wikipedia.org/wiki/Data_Science_and_Predictive_Analytics DSPA Wikipedia].
-* [https://www.socr.umich.edu/people/dinov/courses/DSPA_Topics.html DSPA Appendices]: Bayesian Simulation, Modeling and Inference » Information-Theoretic Foundation of Statistical Learning » Surface, Shape, and Manifold Representation and Visualization » Power Analysis in Experimental Design » Database SQL/NoSQL Queries & Google BigQuery » Image Convolution, Filtering, & Fourier Transform » Causality, Transfer Entropy, & Mechanistic Effects » Agent-based Reinforcement Learning.
+* [https://www.socr.umich.edu/DSPA2/index.html#Appendix DSPA Appendices]: Bayesian Simulation, Modeling and Inference » Information-Theoretic Foundation of Statistical Learning » Surface, Shape, and Manifold Representation and Visualization » Power Analysis in Experimental Design » Database SQL/NoSQL Queries & Google BigQuery » Image Convolution, Filtering, & Fourier Transform » Causality, Transfer Entropy, & Mechanistic Effects » Agent-based Reinforcement Learning.
 * [https://www.springer.com/us/book/9783319723464 DSPA Springer Page] & [http://link.springer.com/978-3-319-72347-1 SpringerLink (PDF Download)].
 * [https://dspa2.predictive.space/ dspa2.predictive.space] & [https://umich.instructure.com/courses/143011/ DSPA MOOC Canvas Site].