This course introduces students to the methods involved in turning real objects into virtual ones using cutting edge digital imaging technology and image rendering techniques. Focusing on manuscripts, paintings, maps, and 3D artifacts, students will learn the basics of Multispectral Imaging, Infrared Thermography, Reflectance Transformation Imaging, focus stacking, spectral image processing using ENVI, stitching in 2D and 3D, and Structure-from-Motion photogrammetry. These skills will be applied to data from the ongoing research of the Lazarus Project as well as to local cultural heritage collections. This course may be taken alone or in conjunction with Image, Text, and Technology (English).

BUILDING: MEL | ROOM: 206

This course is a continuation of CSC262, covering intermediate statistical methodology and related computational methods, with an emphasis on the R statistical computing environment.

BUILDING: CSB | ROOM: 209

PREREQUISITES: CSC 262 AND MTH 165 or MTH 173 or MTH 235

Image, Text and Technology is an interdisciplinary course in the history of the book as a textual and visual medium, an artistic object, and a technology of communication. We will treat this history not merely in the classroom, but participate in it through a series of hands-on projects. Beginning with Aristotle’s insight that we think in images, we will consider writing as bound up in a theoretical relationship with seeing (aesthetics), perceiving (phenomenology, vision, cognitive science), and historically with technologies of dissemination, both analog and digital (manuscripts, printing, photography, television, the internet). We will explore the limits and conjunctions of visual and verbal media through theoretical and scientific readings in Plato, Lessing, Benjamin, Derrida, and McLuhan, and primary texts including the Bible, the Popol Vuh, and the Precepts of Ptah Hotep. This course may be taken alone or in conjunction with Digital Imaging: Transforming Real into Virtual (Digital Media Studies).

BUILDING: RRLIB | ROOM: 416

The course will begin with an introduction to practical aspects relevant to time series data analysis such as data collection, characterization, and preprocessing. Core topics covered will include smoothing methods (moving average, exponential smoothing), trend and seasonality in regression models, autocorrelation, AR and ARIMA models. The course emphasizes an applied end-to-end analysis and forecasting approach rather specific algorithms or models. Students shall work on projects with realistic time-series data sets using visualization and modeling tools: Tableau and R.

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PREREQUISITES: MTH 165; Familiarity with programming

Students are expected to work on a large scale data analysis and mining project. An existing large database is used. Project categories include medical data analysis & social media data analysis. Data mining algorithms are applied to data that is contained in these databases to predict health hazards or social behavior.

BUILDING: HARK | ROOM: 210

PREREQUISITES: This course is only open to data science MS students.

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