Difference between revisions of "Main Page"
Jump to navigation
Jump to search
Line 50: | Line 50: | ||
*Causal Inference - Dr. Walter Dempsey | *Causal Inference - Dr. Walter Dempsey | ||
-[https://sph.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=6df9ea5c-c760-4001-89d6-ad4e013004cb Recording] | -[https://sph.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=6df9ea5c-c760-4001-89d6-ad4e013004cb Recording] | ||
+ | -[https://drive.google.com/file/d/1YuBshVZ-06JjZM9RG6SjZxhBZAyJUSIh/view?usp=sharing Causal Inference Slides] | ||
*Model Selection - Nicky Wakim | *Model Selection - Nicky Wakim | ||
-[https://umich.zoom.us/rec/share/kMgiz3jT24I-KEKC0PqpgTojLOvbwxY0v9NjEy1DYFpzVoSy8hXGoz5v3cmFFlMs._v8fupSuq--thOrl?startTime=1624281763000 Recording] | -[https://umich.zoom.us/rec/share/kMgiz3jT24I-KEKC0PqpgTojLOvbwxY0v9NjEy1DYFpzVoSy8hXGoz5v3cmFFlMs._v8fupSuq--thOrl?startTime=1624281763000 Recording] |
Revision as of 18:34, 23 June 2021
Welcome to the U-M Big Data Summer Institute 2021 Wiki!
Contents
Electronic Health Records Group
Machine Learning Group
Imaging Group
Data Mining Group
Lectures
Week 1
- Study Design and Inference, Observational Data and Bias, Parameter Estimation and Likelihood - Dr. Rod Little
-Part 1, Part 1 Slides -Part 2, Part 2 Slides -Part 3, Part 3 Slides -Part 4, Part 4 Slides -Part 5, Part 5 Slides
- Linear Algebra - Lap Sum Chan
-Recording -Linear Algebra Slides
- Linear Regression - Fatema Shafie Khorassani
-Recording -Linear Regression Slides
- Probability - Rupam Bhattacharyya
-Recording -Probability Slides
- Data Wrangling in R with dplyr - Dr. Matthew Flickinger
-Lecture Slides
- Data Visualization in R with ggplot2 - Dr. Matthew Flickinger
-Lecture Slides
Week 2
- Machine Learning - Dr. Jenna Wiens
- Data Mining - Jiong Zhu
- Logistic Regression - Aubrey Annis
-Recording
- Generalized Linear Models - Aubrey Annis
-Recording
- R Markdown - Dr. Phil Boonstra
-Synchronous Lecture Recording
- Python - Dr. Fred Feng
-Synchronous Lecture Part I and II Recording -Synchronous Lecture Part III and IV Recording
Week 3
- Assessment of Predictive Models - Dr. Phil Boonstra
-Recording
- Causal Inference - Dr. Walter Dempsey
-Recording -Causal Inference Slides
- Model Selection - Nicky Wakim
-Recording -Model Selection Slides -Annotated Model Selection Slides
Week 4
- Introduction to Bayes - Dr. Xiaoquan William Wen
- Missing Data - Dr. Peisong Han
- Clustering - Fahad Kamran
Week 5
- Social Networks - Dr. Eytan Adar
- Electronic Health Records - Dr. Xu Shi
- Genetics/Genomics - Dr. Jean Morrison
- Health Disparities in Strokes - Dr. Lynda Lisabeth
- Precision Health - Dr. Sachin Kheterpal
Week 6
- Data Integration and Precision Medicine - Dr. Veera Baladandayuthapani
- Radiation Oncology - Dr. Arvind Rao
- Better, Not Just Bigger Data Analytics - Dr. Brahmajee Nallamothu
- Clinical Trials - Dr. Kelley Kidwell
Week 7
- Preparing for Graduate School - Dr. Kelley Kidwell
- CVs and Resumes - Krystle Forbes
- Scientific Writing - Dr. Brett Griffiths
- Pick Me! - Dr. Brett Griffiths
Week 8
Journey Lectures
Past BDSI Student Poster Presentations
2019 Student Poster Presentations
2018 Student Poster Presentations
- Imaging Group Presentation
- Machine Learning Group Presentation
- Genetics Group Presentation
- Data Mining Presentation
2017 Student Poster Presentations
2017 Symposium Lecturers
- A Time-to-Event Analysis of Heart Failure via Electronic Health Records
- Melanoma Detection by Classifying Skin Lesion Images
- Classifying Skin Lesions Images Using Adaptive Boosting
- Machine Learning Classification of Skin Lesion Images
- Genomics: Genome Storage and Assembly
- Predicting the Transcriptome from the Genome
- Classification of Cell Types from Peripheral Mononuclear Blood Cells
- EHR-Based Study of Long-Term Infectious Diseases
- Visualizing Lab and Phenotype Associations Using PheWAS and Electronic Health Records
- Data Mining: Microenvironment Microarray Spot Based Approach for Cell Prediction
- Estimating Cell Growth with Machine Learning and Data Mining