|M/W 2:30-3:50 PM|
|M/W 3:50-4:45, or by appointment|
|Monday, May 9th, 1:30-3:30pm|
Have you ever wondered ...
Yes, then this class is for you.
As companies amass more and more data, it becomes increasingly important to be able to move beyond typical database CRUD functions (Create, Report, Update, Delete). This data is generally not very valuable unless we are able to recognize the patterns in it that can lead to actionable intelligence; this is especially true in the case of Big Data (massive datasets). In Big Data Science, we will focus on the practical issues associated with extracting such actionable intelligence.
This includes investigating a variety of machine learning (ML) algorithms (e.g., for classification, function approximation, clustering, attribute association, etc.), learning how to design scientifically sound experiments to test hypotheses, understanding practical issues involved in selecting and tuning ML algorithms and how they satisfy the needs of stakeholders, and learning about issues in data preprocessing, feature engineering, feature selection and visualization.
We will also briefly discuss advanced techniques such as semi-supervised learning, and active learning. The course will cover algorithm frameworks, problem settings, learning objectives, practical considerations, applications, and enough theory to understand the implications of utilizing various algorithms.