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

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− | + | ==Program Schedule== | |

{| class="wikitable" | {| class="wikitable" | ||

− | ! | + | ! Day !! Time !! Instructor !! Session Topic !! Content |

|- | |- | ||

− | | rowspan=" | + | | 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 |

|- | |- | ||

− | | | + | | || || || Why data science, artificial intelligence, and machine learning? |

|- | |- | ||

− | | | + | | 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 |

|- | |- | ||

− | | | + | | || || || Linear Algebra and Matrix Computing |

|- | |- | ||

− | | | + | | || || || Optimization theory |

|- | |- | ||

− | | | + | | || || || Differential Equations |

|- | |- | ||

− | | | + | | || || || |

|- | |- | ||

− | | | + | | 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 10:20, 20 April 2021

## Contents

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

**Dates/Times**: Monday through Friday, July 26-20, 2021, 7:00-16:00 US-EDT (daily).**Registration**: Registration Link.**URL**: MIDAS Bootcamp Website.**Session Format**: Two daily sessions (3-hours each).- 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

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

- Course Flyer
- DSPA Wikipedia.
- DSPA Springer Page & SpringerLink (PDF Download).
- dspa.predictive.space & DSPA MOOC Canvas Site.

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