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Department ofComputer Science

Supplemental Courses

Here is where additional course information appears, especially regarding special topics courses (CS 525 that do not appear in the course catalog.

Professor: Rob Lindeman

This course focuses on the design and evaluation of Augmented Reality (AR) systems, algorithms, and applications. With the proliferation of powerful, always-on, Internet-connected mobile devices such as smartphones, tablets and newer head-worn displays, sophisticated applications that combine location-specific content with the current user view are becoming more possible. Application developers for these devices require a broad set of technical and design skills to create effective interactive AR experiences. Topics will include vision-based marker and feature tracking, model-to-view space transformations, mobile application development, and AR interaction techniques.

Through a combination of traditional lecture, literature review, and hands-on work, students will learn to critically evaluate different alternatives, build prototype systems, and design comparative evaluations to test the effectiveness of various AR applications. Students will be expected to implement several techniques as part of this course. (Prerequisites: The ability to program in a higher-level language. Suggested background: a course on computer graphics, computer vision and/or human-computer interaction).

CS 525-B. BIG DATA ANALYTICS (Fall 2013)
Professor: Elke Rundensteiner

We are living in the age of big data, where data is measured in terabytes, streamed in real-time, and derived at unprecedented speeds in diverse forms. Big data promises to impact the world as we know it, from increased productivity at our workplace, yet unknown types of innovation, to how we live our daily social lives. However, it also presents tremendous challenges, as various entities strive to harness and gain insights from vast torrents of complex data. This course investigates computational techniques for analyzing and mining patterns in massive-scale data. Techniques studied may include data analysis issues related to data volume; data velocity, including high speed data streams; data variety such as complex, unstructured, and distributed data; and data veracity such as data inconsistency and noise. Scale-up and scale-out strategies as well as strategies that leverage advanced database infrastructure and high performance computing are considered. Techniques in advanced mining and their application to real-world problems, such as social media and text mining, will be discussed. As part of this course, we will read state-of-the-art literature as well as try our hands on this technology by conducting a course project.

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