This school is about the algorithmic, statistical, and engineering challenges associated with various stages of data analysis. Each of the sub-topics will include both theoretical and hands-on aspects. We will cover how to collect and clean up data, probabilistic models for data, and various algorithmic challenges that arise when scaling these models to large data. We will also do deep dives for data-driven modeling in three different scientific domains – natural language processing, computer vision, and earth and climate sciences. Lastly, we will learn how to deploy a machine learning model in production and keep it up-to-date. There will be multiple lectures on each subtopic, and participants will be taken from the basics to some of the cutting-edge questions in these areas.
Please follow the webpage: ACM-IKDD Summer School on Data Science