Overview

This course will provide an introduction to recent research in the area of multi-source learning. The course will survey recent approaches to model multi-source datasets, focusing on fundamental challenges in representation, learning, and inference. The class will be highly interactive, collaborative, and hopefully fun! The bulk of the work will be a class project (which ideally relates to your research and results in a publication), and reading and presenting papers.

Learning objectives

• Learn about Multi-source Learning
• Work on a research project in the area of Multi-source Learning
• Sharpen necessary skills for conducting research including: (1) authoring, reviewing and presenting research papers, (2) defining and authoring small research proposals, (3) reviewing a research topic and authoring a research survey paper, (4) participating in research discussions, and (5) collaborating on research.

Background

The suggested background includes machine learning and data analytics.

Projects

Students are encouraged to propose a project that relates to their research while leveraging multi-source learning. Other potential projects include: investigating the performance of multi-source learning algorithms, analyzing data with multi-source learning models, designing and implementing multi-source learning model/algorithm extensions, and a survey paper on a chosen problem or class of techniques.