Difference between revisions of "Main Page"
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Revision as of 00:43, 4 June 2019
Welcome to the U-M Big Data Summer Institute 2019 Wiki!
Contents
- 1 Reading Material
- 2 2019 Presentations
- 3 Day 29: Symposium
- 4 Additional Resources
Reading Material
Machine Learning Group
Research Lecture Slides
- Introduction
- Explore MIMIC
- Getting x and y
- Some tips
- Sample Pipeline
- Training Pipleline
- CNN
- LSTM
- Structuring
- Dataset and DataLoader
- pytorch models
- Model Development
- Population
- Benchmark Features
- Inclusion Exclusion
Lecture Notes
- Recurrent Neural Networks
- Convolutional Neural Networks
- Deep Learning
- BDSI Lecture
- Flux Guide
- Python Guide
- Python Tutorial
Readings
- ARF Epidemiology
- CNN for Sentence Clarification
- Learning from Heterogenous Temporal Data
- MIMC 3
- MIMIC Benchmarks and Multitask RNN
- RNNs for Multivariate Time Series
- TREWScore for Septic Shock
- TREWScore Supplement
Genomics Group
Lectures
Intro Exercises
Papers
Population Genetics
Single Cell RNA
Transcriptomics
Online videos to better understand genetics and genomics
Genetics
- Introduction to Genetics by 23andMe (5 videos)
- TED-Ed : How Mendel's pea plants helped us understand genetics - Hortensia Jiménez Díaz
- Genetic Recombination and Gene Mapping by Bozeman Science
- Useful Genetics : A college-level comprehensive genetics course with 292 lectures offered by Rosie Redfield at UBC
Useful 3D Animations
- From DNA to protein - 3D Animation
- DNA Transcription - 3D Animation
- DNA splicing - 3D Animation
- mRNA Translation - 3D Animation
- How DNA is packaged - 3D Animation
- The Central Dogma - 3D Animation
Gene Regulation and Epigenetics
- Epigenetics Lecture by SciShow
- Hi-C Technique : A 3D map of the Human Genome
- The ENCODE Project
- RNAi by Nature Video
Sequencing Technologies
- TED-Ed : The race to sequence the human genome - Tien Nguyen
- DropSeq - Droplet-based Single Cell Sequencing by McCarroll Lab
Data Mining on Large Complex Datasets
Papers
2019 Presentations
Week 1
Day 1: June 17
Day 2: June 18
Recorded Lectures
Day 3: June 19
Recorded Lectures
Day 4: June 20
Recorded Lectures
Day 5: June 21
Recorded Lectures
Day 6: June 22
Recorded Lectures
Week 2
Day 7: June 25
Recorded Lectures
Day 8: June 26
Recorded Lectures
Day 9: June 27
Day 10: June 28
Recorded Lectures
Day 11: June 29
Recorded Lectures
Week 3
Day 12: July 2
Recorded Lectures
Day 13: July 3
Recorded Lectures
Day 14: July 4 (NO CLASS)
Day 15: July 5
Recorded Lectures
Day 16: July 6
Recorded Lectures
Week 4
Day 17: July 9
Recorded Lectures
Day 18: July 10
Recorded Lectures
Day 19: July 11
Recorded Lectures
Day 20: July 12
Day 21: July 13
Recorded Lectures
Week 5
Day 22: July 16
Recorded Lectures
Day 23: July 17
Recorded Lectures
Day 24: July 18
Recorded Lectures
Day 25: July 19
Recorded Lectures
Day 26: July 20
Recorded Lectures
Day 27: July 20
Recorded Lectures
Day 29: Symposium
2019 Professor Lectures Presentations
2019 Student Poster Presentations
2018 Professor Lectures Presentations
- 2018 Symposium Flyer
- Symposium Welcome Remarks
- Calibration Concordance - Chen
- Data Science and Predictive Health Analytics - Dinov
- Models of Human Choice - Feinberg
- Linking Tumor with Personalized Medicine - Rao
- Humanist Approach to Data Science - Schutt
- Big Data in the Social Sciences - Titunik
- Detecting Epistasis in Large Scale Genetic Mapping - Zhou
2018 Student Poster Presentations
- Imaging Group Presentation
- Machine Learning Group Presentation
- Genetics Group Presentation
- Data Mining Presentation
2017 Student Poster Presentations
2017 Symposium Reference Files
2017 Symposium Projects
- 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
Additional Resources
- DataCamp Resources
- [Daily Schedule] - Last update July 3, 2018
