Loading…
This event has ended. Visit the official site or create your own event on Sched.
Click here to return to main conference site. For a one page, printable overview of the schedule, see this.
avatar for Jakob Richter

Jakob Richter

TU Dortmund University
- Interested in Machine Learning and Hyperparameter Tuning/Optimization.
- Especially through Model Based Optimization using Kriging or Random Forest.
- Parallel Computation
Monday, June 27
 

9:00am PDT

Never Tell Me the Odds! Machine Learning with Class Imbalances (Part 1) Econ 140

10:30am PDT

Never Tell Me the Odds! Machine Learning with Class Imbalances (Part 2) Econ 140

1:00pm PDT

Extracting data from the web APIs and beyond (Part 1) Campbell Rehearsal Hall Understanding and creating interactive graphics (Part 1) McDowell & Cranston

2:30pm PDT

Extracting data from the web APIs and beyond (Part 2) Campbell Rehearsal Hall Understanding and creating interactive graphics (Part 2) McDowell & Cranston
 
Tuesday, June 28
 

10:30am PDT

R in machine learning competitions McCaw Hall

11:06am PDT

trackeR: Intrastructure for running and cycling data from GPS-enabled tracking devices in R McCaw Hall

11:42am PDT

FlashR: Enable Parallel, Scalable Data Analysis in R SIEPR 130

12:00pm PDT

Lunch with Poster Exhibits Sponsor Pavilion

1:54pm PDT

A first-year undergraduate data science course SIEPR 130

2:12pm PDT

Teaching R to 200 people in a week SIEPR 130

2:30pm PDT

Coffee Break w/ Poster Session McCaw Hall

3:30pm PDT

Literate Programming McCaw Hall

4:30pm PDT

Short Break McCaw Hall

4:45pm PDT

mlrMBO: A Toolbox for Model-Based Optimization of Expensive Black-Box Functions SIEPR 130

6:30pm PDT

Welcome Reception Bing Concert Hall
 
Wednesday, June 29
 

9:00am PDT

Towards a grammar of interactive graphics McCaw Hall

11:06am PDT

Resource-Aware Scheduling Strategies for Parallel Machine Learning R Programs though RAMBO Econ 140

1:54pm PDT

Checkmate: Fast and Versatile Argument Checks SIEPR 130
 
Thursday, June 30
 

10:30am PDT

Gradient Boosted Trees Model: deploying R models into production environments* Lane & Lyons & Lodato

10:50am PDT

ranger: A fast implementation of random forests for high dimensional data Lane & Lyons & Lodato

11:08am PDT

Superheat: Supervised heatmaps for visualizing complex data Lane & Lyons & Lodato

11:26am PDT

Big data algorithms for rank-based estimation Lane & Lyons & Lodato