Corrie BartelheimerData Science, Statistics
An Example of a Bayesian Workflow using PyMC and ArviZ: Predicting House Prices in Berlin
An approach for a matrix completion problem using the Bayesian Nonnegative Matrix Factorization (NMF).
Franziska HornData Science, Machine Learning, Science, Data Engineering, Statistics
Automated feature engineering and selection in Python with the autofeat library.
Stefan MaierAlgorithms, Data Science, Machine Learning, Statistics
Usually, uncertainties of Machine Learning predictions are just regarded as a sign of poor prediction accuracy or as a consequence of lacking input features. This talk illustrates how modeling uncertainties can improve ML based decisions.
Marie-Louise TimckeBig Data, Data Science, Statistics
Marie will talk about how newsrooms work with data on a day to day basis, and how scientific accuracy fits in with the pace of news reporting.
Yuta KanzawaData Science, Machine Learning, Visualisation, Statistics
R and Python are different in community and as language. Still, comparing them in their common fields such as data wrangling and visualisation, useRs and Pythonistas will deepen mutual understanding.
Dr. Juan OrduzAlgorithms, Data Science, Machine Learning, Statistics
Gaussian process for regressions problems and time series forecasting
Vincent WarmerdamArtificial Intelligence, Algorithms, Data Science, IDEs/ Jupyter, Machine Learning, Statistics
gaussian progress. it's meta, but also the most normal conference title this year!
Caio MiyashiroAlgorithms, Data Science, Machine Learning, Statistics
Come check out Caio's workshop on music+programming+stats on PyData
Maximilian EberData Science, Machine Learning, Science, Statistics
How to use machine learning to evaluate randomised experiments and A/B tests
Korbinian KuusistoAlgorithms, Business & Start-Ups, Data Science, Machine Learning, Science, Statistics
How can one leverage the power of Bayesian methods to build a successful data science product?
David WölfleArtificial Intelligence, Algorithms, Deep Learning, Data Science, Machine Learning, Statistics
This talk covers the theoretical background behind two common loss functions, mean squared error and cross entropy, including why they are used for machine learning at all, and what limitations you should keep in mind.
Benedikt RudolphAlgorithms, Business & Start-Ups, Data Science, Science, Statistics
Learn about a simple least-squares approach to evaluate financial exercise options and make optimal exercise decisions.
Sean Matthews, Jannes QuerData Science, Statistics
Probabilistic time-series forecasting @ Deloitte Analytics Institute
Yurii TolochkoArtificial Intelligence, Algorithms, Deep Learning, Machine Learning, Statistics
Why doesn’t RL show the same success as (un)supervised learning? Inherent difficulties facing RL and avenues for future work