This course provides a hands-on experience with tools and methods in the field of machine learning, data science, and analytics. Many useful insights can be derived from data. Ubiquitous data naturally exist in a variety of disciplines and application domains ranging from computer science, social science, economics, and medicine to bioinformatics. Example datasets include the WWW, social networks, biological networks, communication networks, transportation networks, energy grids, knowledge graphs, social media, finance data, citation data, marketing data, and molecular structures. In the real world, data is multimodal, multi-relational, dynamic, large - with many complex patterns - and noisy which can pose a problem for effective mining and/or learning. .

Course overview

The course focuses on analyzing and learning from data. The key objectives of this course are to learn how to (1) formulate a real-world problem as a machine learning one, (2) work through the complete data science process from data to knowledge: collect, process, store, analyze and learn from data, (3) collaborate on research through a team term project, (4) evaluate, visualize, articulate and present research findings, and (5) build on skills acquired from previous courses. Students can propose a project of their choice or the instructor can help supply ideas to choose from.