The International Online Seminar Series on Data Science
Streaming on Youtube (9PM New York time, Jan 18, 2020). Register below to receive the information.
Cloud-based Electric Load Forecasting with BigQuery ML
Wei Kou, Ph.D.
Machine Learning Software Engineer, GE Digital
In this talk, Dr. Kou implements the electric load prediction in the Bigquery ML cloud platform as well as compares its performance with the linear regression model in python scikit-learn.
Data Science, GCP, BigQuery ML, Cloud
About the speaker
Dr. Wei Kou received a Ph.D. degree in Electrical and Computer Science Engineering from the University of Connecticut in 2018. Her research interests include cloud-based machine learning, game theory, distributed optimization and the related application on power grids and energy networks.
The two cultures: Retrospects and prospects, Son P. Nguyen, University of Economics and Law, VNU-HCM (Nov 30, 2019)
In 2001, prominent statistician Leo Breiman wrote a renowned paper titled “Statistical modeling: the two cultures" in which he described two seemingly contrasting approaches to data analysis, namely
1. Data modeling: select generative models with emphasis on interpretability, and, to some extent, causality.
2. Algorithmic modeling: select models with best predictive capabilities (via validation) with little or no consideration to model explainability.
In this talk, after briefly reviewing Breiman's ideas, we will discuss some reasons why both approaches are needed in modern data science. Moreover, to get the best of both worlds, we would like a unified framework which allows data scientists to exibly take advantage of both types of modeling. Next, we will introduce fundamentals of probabilistic programming languages with a few examples to illustrate the combinations.