Difference between revisions of "SOCR News MIDAS Biomedical Bootcamp 2021"

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(Created page with "== SOCR News & Events: 2021 MIDAS Data Science for Biomedical Scientists Bootcamp == Image:DSPA_ISI_WSC_anime.gif|right| [https://midas.umich.edu/data-scie...")
 
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===Program Schedule===
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==Program Schedule==
  
 
{| class="wikitable"
 
{| class="wikitable"
−
! Areas !! Competency !! Expectation !! Notes
+
! Day !! Time !! Instructor !! Session Topic !! Content
 
|-
 
|-
−
| rowspan="3"|Algorithms and Applications || Tools || Working knowledge of basic software tools (command-line, GUI based, or web-services) || Familiarity with statistical programming languages, e.g., R or SciKit/Python, and database querying languages, e.g., SQL or NoSQL
+
| 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
 
|-
 
|-
−
| Algorithms || Knowledge of core principles of scientific computing, applications programming, API’s, algorithm complexity, and data structures || Best practices for scientific and application programming, efficient implementation of matrix linear algebra and graphics, elementary notions of computational complexity, user-friendly interfaces, string matching
+
| || ||  || Why data science, artificial intelligence, and machine learning?
 
|-
 
|-
−
|  Application Domain || Data analysis experience from at least one application area, either through coursework, internship, research project, etc. || Applied domain examples include: computational social sciences, health sciences, business and marketing, learning sciences, transportation sciences, engineering and physical sciences
+
|  8:30 - 8:45 AM || colspan="3"|Break
 
|-
 
|-
−
| rowspan="3"|Data Management || Data validation & visualization || Curation, Exploratory Data Analysis (EDA) and visualization || Data provenance, validation, visualization via histograms, Q-Q plots, scatterplots (ggplot, Dashboard, D3.js)
+
| 8:45 - 10:15 AM || Ivo Dinov || Session 2: Math foundations I – Brief introduction to mathematical foundations of machine learning || Math notation and fundamentals
 
|-
 
|-
−
| Data wrangling || Skills for data normalization, data cleaning, data aggregation, and data harmonization/registration || Data imperfections include missing values, inconsistent string formatting (‘2016-01-01’ vs. ‘01/01/2016’, PC/Mac/Linux time vs. timestamps, structured vs. unstructured data
+
|   ||  ||  || Linear Algebra and Matrix Computing
 
|-
 
|-
−
| Data infrastructure || Handling databases, web-services, Hadoop, multi-source data || Data structures, SOAP protocols, ontologies, XML, JSON, streaming
+
|   || || || Optimization theory
 
|-
 
|-
−
| rowspan="3"|Analysis Methods || Statistical inference || Basic understanding of bias and variance, principles of (non)parametric statistical inference, and (linear) modeling || Biological variability vs. technological noise, parametric (likelihood) vs non-parametric (rank order statistics) procedures, point vs. interval estimation, hypothesis testing, regression
+
|   || || || Differential Equations
 
|-
 
|-
−
| Study design and diagnostics || Design of experiments, power calculations and sample sizing, strength of evidence, p-values, False Discovery Rates || Multistage testing, variance normalizing transforms, histogram equalization, goodness-of-fit tests, model overfitting, model reduction
+
|   || ||  ||  
 
|-
 
|-
−
| Machine Learning || Dimensionality reduction, k-nearest neighbors, random forests, AdaBoost, kernelization, SVM, ensemble methods, CNN || Empirical risk minimization. Supervised, semi-supervised, and unsupervised learning. Transfer learning, active learning, reinforcement learning, multiview learning, instance learning
+
| 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
 +
|-
 +
|  ||  ||  || Principal Component Analysis (PCA)
 +
|-
 +
|  ||  ||  || 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 || Session 6: Linear regression, logistic regression || Simple linear regression, logit modeling
 +
|-
 +
| ||  ||  || Ordinary least squares estimation
 +
|-
 +
|  ||  ||  || Example scenarios
 +
|-
 +
|  ||  ||  ||
 +
|-
 +
|  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="10"|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="0"|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="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
 +
|-
 +
|  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
 
|}
 
|}
  

Revision as of 09:20, 20 April 2021

SOCR News & Events: 2021 MIDAS Data Science for Biomedical Scientists Bootcamp

2021 MIDAS Biomedical Bootcamp


The 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

Kayvan Najarian
Nambi Nallasamy
Ivo Dinov, University of Michigan, SOCR, MIDAS.
Michael Mathis
Ryan Stidham
Jonathan Gryak
Michael Sjoding


Workshop Logistics

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

Day Time Instructor Session Topic Content
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 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
Linear Algebra and Matrix Computing
Optimization theory
Differential Equations
10:15 - 10:30 AM Break
10:30 AM - 12:00 PM Ivo Dinov Session 3: Math foundations II – Brief introduction to mathematical foundations of machine learning Dimensionality
Principal Component Analysis (PCA)
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 Break
2:45 - 4:15 PM TBA Session 5: Introduction to Python programming Basics of Python programming
Tuesday 7:00 - 8:30 AM Ivo Dinov Session 6: Linear regression, logistic regression Simple linear regression, logit modeling
Ordinary least squares estimation
Example scenarios
8:30 - 8:45 AM 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 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 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
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 Break
8:45 - 10:15 AM Kayvan Najarian Session 12: Regression trees Classification and regression tree (CART)
10:15 - 10:30 AM 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 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 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
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 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 Break
10:45 AM - 12:00 PM Jonathan Gryak Session 18: Deep Learning II LSTM, Autoencoders
12:00 - 1:00 PM 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 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
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 Break
8:45 - 10:15 AM Michael Sjoding Session 22: Using machine learning for clinical and health applications III
10:15-10:30 AM 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 Lunch Break
1:00 - 2:30 PM Michael Mathis Session 24: Guidelines on using machine learning for clinical applications
2:30 - 2:45 PM 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


Additional Resources


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