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- informatics.usc.edu D class assignments for graduate students are only available on line at: myviterbi.usc.edu. Once you create your myViterbi profile, select the "D-Clearance Request Manager" to submit requests for graduate INF courses. To be enrolled in an off-campus course, you MUST also be enrolled in the Distance Education Network (DEN). For more information, call 740-4488 or go to den.usc.edu. DEN courses are indicated by a location of DEN@Viterbi
Assurance that an information system will behave as expected; assurance approaches for fielding secure information systems; case studies. Recommended preparation: Prior degree in computer science, electrical engineering, computer engineering, management information systems, and/or mathematics. Some background in computer security preferred.
- Prerequisite: INF 519
|32426R||048||Discussion||TBA||TBA||0 of 25||OFFICE|
|32432R||034||Discussion||TBA||TBA||0 of 25||DEN@Viterbi|
Fundamentals of big data informatics techniques. Data lifecycle; the data scientist; machine learning; data mining; NoSQL databases; tools for storage/processing/analytics of large data set on clusters; in-data techniques. Recommended preparation: A basic understanding of engineering principles and programming language is desirable.
|32430D||048||Lecture||3:30-5:20pm||Mon, Wed||0 of 36||Seon Kim||ZHS163|
Graphical depictions of data for communication, analysis, and decision support. Cognitive processing and perception of visual data and visualizations. Designing effective visualizations. Implementing interactive visualizations.
|32421D||048||Lecture||2:00-5:20pm||Wednesday||0 of 40||Luciano Nocera||VKC150|
Foundations, techniques, and algorithms for building knowledge graphs and doing so at scale. Topics include information extraction, data alignment, entity linking, and the Semantic Web.
|32438D||048||Lecture||2:00-5:20pm||Friday||0 of 50||Craig Knoblock,Pedro Szekely||GFS116|
Theory and methods of data analytics emphasizing engineering applications: multivariate statistics, supervised learning, classification, smoothing and kernel methods, support vector machines, discrimination analysis, unsupervised learning.
- Crosslist: This course is offered by the ISE department but may qualify for major credit in INF. To register, enroll in ISE 529.
|31529D||048||Lecture||3:30-6:20pm||Friday||0 of 20||Cesar Acosta-Mejia||KAP134|
Medical imaging quality, compression, data standards, workflow analysis and protocols, broadband networks, image security, fault tolerance, picture archive communication system (PACS), image database and backup.
- Crosslist: This course is offered by the BME department but may qualify for major credit in INF. To register, enroll in BME 527.
|29305D||034||Lecture||9:00-11:50am||Friday||0 of 20||Brent Liu||DEN@Viterbi|
|29310D||048||Lecture||9:00-11:50am||Friday||0 of 28||Brent Liu||OHE100B|
Research leading to the master's degree; maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.
- Restriction: Registration open to the following major(s): INF
|32449D||048||Lecture||TBA||TBA||0 of 22||OFFICE|