SOCR News MIDAS Biomedical Bootcamp 2021 - Revision history

Dinov: /* Additional Resources */

2021-07-12T22:30:42Z

‎Additional Resources

Dinov: /* Additional Resources */

2021-07-12T22:28:16Z

‎Additional Resources

Dinov at 22:26, 12 July 2021

2021-07-12T22:26:54Z

Dinov: /* Workshop Logistics */

2021-06-30T14:50:40Z

‎Workshop Logistics

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2021-06-30T13:59:44Z

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Dinov: /* Workshop Logistics */

2021-06-30T13:58:04Z

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Dinov at 16:15, 16 June 2021

2021-06-16T16:15:10Z

Dinov at 15:17, 20 April 2021

2021-04-20T15:17:52Z

Dinov: /* Program Schedule */

2021-04-20T14:22:49Z

‎Program Schedule

Dinov at 14:20, 20 April 2021

2021-04-20T14:20:20Z

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@@ Line 170: / Line 170: @@
 ==Additional Resources==
 * [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.

@@ Line 171: / Line 171: @@
 ==Additional Resources==
 * [https://en.wikipedia.org/wiki/Data_Science_and_Predictive_Analytics DSPA Wikipedia].
 * [https://www.springer.com/us/book/9783319723464 DSPA Springer Page] & [http://link.springer.com/978-3-319-72347-1 SpringerLink (PDF Download)].
 * [https://dspa.predictive.space/ dspa.predictive.space] & [https://umich.instructure.com/courses/143011/ DSPA MOOC Canvas Site].

@@ Line 2: / Line 2: @@
 [[Image:MIDAS_Bootcamp_anime4.gif|right| [https://midas.umich.edu/data-science-for-biomedical-scientists/ 2021 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.
@@ Line 50: / Line 48: @@
 |  8:30 - 8:45 AM || colspan="3"|Break
 |-
-|  8:45 - 10:15 AM || Ivo Dinov || Session 2: Math foundations I – Brief introduction to mathematical foundations of machine learning || Math notation and fundamentals
+|  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
 |-
-|   ||  ||  || Linear Algebra and Matrix Computing
+|   ||  ||  || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/04_LinearAlgebraMatrixComputing.html Linear Algebra and Matrix Computing]
 |-
-|   ||  ||  || Optimization theory
+|   ||  ||  || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/21_FunctionOptimization.html#10_Practice_examples Optimization theory]
 |-
-|   ||  ||  || Differential Equations
+|   ||  ||  || [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 || Dimensionality
+|  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]
 |-
-|   ||  ||  || Principal Component Analysis (PCA)
+|   ||  ||  || [https://www.socr.umich.edu/people/dinov/courses/DSPA_notes/05_DimensionalityReduction.html#1_Example:_Reducing_2D_to_1D Principal Component Analysis (PCA)]
 |-
-|   ||  ||  || High-dimensional Visualization (hands-on demos)
+|   ||  ||  || [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 ||  ||
@@ Line 80: / Line 78: @@
 |  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 || Session 6: Linear regression, logistic regression || Simple linear regression, logit modeling
+| 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
@@ Line 86: / Line 84: @@
 |  ||  ||  || 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
@@ Line 166: / Line 164: @@
 |  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
 |}
 ==Additional Resources==

@@ Line 21: / Line 21: @@
 * '''Registration''': [https://forms.gle/U7FBrmbghfMFPuLr6 Registration Link].
 * '''URL''': [https://midas.umich.edu/data-science-for-biomedical-scientists/ MIDAS Bootcamp Website].
-* '''Session Format''':  Two daily sessions (3-hours each).
+* '''Session Format''':  Two daily sessions (4-hours each).
 * [https://wiki.socr.umich.edu/index.php/SOCR_News_MIDAS_Biomedical_Bootcamp_2021 Session URL].

@@ Line 168: / Line 168: @@
 ==Additional Resources==
 * [https://en.wikipedia.org/wiki/Data_Science_and_Predictive_Analytics DSPA Wikipedia].
 * [https://www.springer.com/us/book/9783319723464 DSPA Springer Page] & [http://link.springer.com/978-3-319-72347-1 SpringerLink (PDF Download)].

@@ Line 18: / Line 18: @@
 ==Workshop Logistics==
-* '''Dates/Times''': Monday through Friday, July 26-20, 2021, 7:00-16:00 [https://www.timeanddate.com/time/zones/et US-EDT] (daily).
+* '''Dates/Times''': Monday through Friday, July 26-30, 2021, 7:00-16:00 [https://www.timeanddate.com/time/zones/et US-EDT] (daily).
 * '''Registration''': [https://forms.gle/U7FBrmbghfMFPuLr6 Registration Link].
 * '''URL''': [https://midas.umich.edu/data-science-for-biomedical-scientists/ MIDAS Bootcamp Website].

← Older revision		Revision as of 16:15, 16 June 2021
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← Older revision		Revision as of 15:17, 20 April 2021
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	== [[SOCR_News \| SOCR News & Events]]: 2021 MIDAS Data Science for Biomedical Scientists Bootcamp ==		== [[SOCR_News \| SOCR News & Events]]: 2021 MIDAS Data Science for Biomedical Scientists Bootcamp ==

−	[[Image:~~DSPA_ISI_WSC_anime~~.gif\|right\| [https://midas.umich.edu/data-science-for-biomedical-scientists/ 2021 MIDAS Biomedical Bootcamp] ]]	+	[[Image:MIDAS_Bootcamp_anime4.gif\|right\| [https://midas.umich.edu/data-science-for-biomedical-scientists/ 2021 MIDAS Biomedical Bootcamp] ]]

@@ Line 44: / Line 44: @@
 ! Day	!! Time	!! Instructor !! Session Topic !! Content
 |-
-| rowspan="16"|Monday || 7:00 - 8:30 AM || Kayvan Najarian || Session 1: Welcome and introduction to the program || A review of the program and logistics
+| 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?
@@ Line 104: / Line 104: @@
 |  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="10"|Wednesday || 7:00 - 8:30 AM || Kayvan Najarian || Session 11: Artificial neural networks I || Fundamentals of artificial neural networks and their advantages/limitations
+| 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
 |-
 |  ||  ||  ||
@@ Line 126: / Line 126: @@
 |  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="0"|Thursday || 7:00 - 8:30 AM || Kayvan Najarian || Session 16: Support vector machines || Using Kernel methods for support vector machines (SVM)
+| 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
@@ Line 146: / Line 146: @@
 | 7:00-8:30 AM ||  ||  ||
 |-
-| rowspan="9"|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
+| 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
@@ Line 166: / Line 166: @@
 |  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
 |}
 ==Additional Resources==