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Welcome to the U-M Big Data 2016 Summer Program Wiki!
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Contents
- 1 Reading Material
- 2 2016 Presentations
- 2.1 Day 1: June 13
- 2.2 Day 2: June 14
- 2.3 Day 3: June 15
- 2.4 Day 4: June 16
- 2.5 Day 5 June 17
- 2.6 Day 6 June 20
- 2.7 Day 7 June 21
- 2.8 Day 8 June 22
- 2.9 Day 9 June 23
- 2.10 Day 10 June 24
- 2.11 Day 11 June 27
- 2.12 Day 12 June 28
- 2.13 Day 13 June 29
- 2.14 Day 14 June 30
- 2.15 Day 15 July 1
- 2.16 Day 17 July 5
- 2.17 Day 18 July 6
- 3 Getting started
Reading Material
Data Mining Group
- Automatic Construction and Natural-Language Description
- Interestingness Measures for Data Mining a Survey
- Fast Algorithms for Mining Association Rules
EHR Group
- Statistical Inference, Learning and Models in Big Data
- USRDS 2015 Annual Data Report, Volume 1: Chronic Kidney Disease
- Dialysis Facility Characteristics and Services
- Data Dictionary for Quarterly Dialysis Facility Compare
- USRDS Introduction to Volume 1: CKD in the United States
Genomics Group
- A Global Reference for Human Genetic Variation
- A Map of Human Genome Variation from Population-Scale Sequencing
2016 Presentations
Day 1: June 13
- Orientation 2016 (slides) - Bhramar Mukherjee, PhD
- Welcome Reception (slides) - Bhramar Mukherjee, PhD
- Computing Resources (slides)
- Ethics Review (slides)
- On Being A Scientist (slides)
- Life in Ann Arbor (slides) - Evan Reynolds, Biostatistics PhD Student
- Intro to Probabilities and Distributions (file) - Rebecca Rothwell, Biostatistics PhD student
Day 2: June 14
- Computing Platforms (website) - Jacob Abernethy, PhD
- Introductory Statistics (slides & audio) - Bhramar Mukherjee, PhD
Day 3: June 15
- Intro to Unix (slides) - Hyun Min Kang, PhD
Day 4: June 16
- Data Structure using Python (website) - Jacob Abernethy, PhD
- Observational Data, Bias and Confounding (slides & audio) - Roderick Little, PhD
Day 5 June 17
- Data Structure using Python (website) - Jacob Abernethy, PhD
- From Pure Mathematics to Gene Discovery: A Biostatistician's Journey (slides & audio) - Michael Boehnke, PhD
Day 6 June 20
- Statistical Modeling using R (slides) - Phil Boonstra, PhD
- Study Design and Inference (slides & audio) - Roderick Little, PhD
Day 7 June 21
- Visualization using R (slides & audio) - Phil Boonstra, PhD
- Distributed Computing (slides) - Harsha Madhyastha, PhD
Day 8 June 22
- Fundamentals of Data Processing (slides) - Jed Carlson, Biostatistics PhD student
Day 9 June 23
- Matrix Computation (audio) - Jason Estes, Research Fellow in Biostatistics
Day 10 June 24
- Career Journey and A Principal Approach to Dimensionality Reduction Part 1 (slides) - Stephen Gliske, PhD
- Career Journey and A Principal Approach to Dimensionality Reduction Part 2 (slides) - Stephen Gliske, PhD
- Career Journey and A Principal Approach to Dimensionality Reduction Part 3 (slides) - Stephen Gliske, PhD
Day 11 June 27
- Large Scale Optimization Part 1 (slides & audio) - Tewari Ambuj, PhD
- link Causal Inference (audio) - Lu Wang, PhD
- Likelihood Functions and Parameter Estimation (slides) - Matthew Zawistowski, Research Specialist at the University of Michigan
Day 12 June 28
- Large Scale Optimization Part 2 (slides & audio) - Tewari Ambuj, PhD
- Sequential Decision Making (slides & audio) - Tewari Ambuj, PhD
Day 13 June 29
- R - dplyr - Matthew Flickinger, Senior Analyst at the University of Michigan
- R - Troubleshooting - Matthew Flickinger, Senior Analyst at the University of Michigan
Day 14 June 30
- Clustering: Graphical Models and Sampling Algorithm Part 1 (slides & audio) - Long Nguyen, PhD
- Clustering: Graphical Models and Sampling Algorithm Part 2 (slides & audio) - Long Nguyen, PhD
Day 15 July 1
- My SMART Journey (slides & audio) - Kelley Kidwell, PhD
- My Spatial Journey to UM Biostatistics (slides & audio) - Veronica Berrocal, PhD
Day 17 July 5
- Supervised Machine Learning (slides & audio) - Hui Jiang, PhD
- Intro to Bayes (slides & audio) - Bhramar Mukherjee, PhD
