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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
Analysis of computer security and why systems are not secure. Concepts and techniques applicable to the design of hardware and software for Trusted Systems.
- Prerequisite: INF 519
|32418D||048||Lecture||12:30-2:20pm||Tue, Thu||11 of 25||Tanya Ryutov||OHE120|
|32419D||034||Lecture||12:30-2:20pm||Tue, Thu||7 of 10||Tanya Ryutov||DEN@Viterbi|
The administrator's role in information system testing, certification, accreditation, operation and defense from cyber attacks. Security assessment. Examination of system vulnerabilities. Policy development. Recommended preparation: Previous degree in computer science, mathematics, computer engineering, informatics, and/or information security undergraduate program. Also, it is highly recommended that students have successfully completed course work involving policy and network security.
- Prerequisite: CSCI 530
|32441D||048||Lecture||2:00-5:20pm||Wednesday||12 of 40||Clifford Neuman||OHE100C|
|32443R||048||Lab||TBA||TBA||12 of 40||OFFICE|
|32442D||034||Lecture||2:00-5:20pm||Wednesday||0 of 20||Clifford Neuman||DEN@Viterbi|
|32444R||034||Lab||TBA||TBA||0 of 20||OFFICE|
Covers societal implications of information privacy and how to design systems to best preserve privacy. Recommended preparation: General familiarity with the use of common internet and mobile applications.
|32412D||048||Lecture||12:00-3:20pm||Friday||15 of 40||Clifford Neuman||OHE100C|
|32413D||034||Lecture||12:00-3:20pm||Friday||0 of 10||Clifford Neuman||DEN@Viterbi|
Function and design of modern storage systems, including cloud; data management techniques; data modeling; network attached storage, clusters and data centers; relational databases; the map-reduce paradigm. Duplicates credit in INF 559. Recommended preparation: INF 550 taken previously or concurrently; understanding of operating systems, networks, and databases; experience with probability, statistics, and programming.
|Seon Kim||PDF (161865 KB)|
|32411D||048||Lecture||3:30-6:20pm||Tuesday||24 of 40||Wensheng Wu||SOSB44||PDF (235582 KB)|
Practical applications of machine learning techniques to real-world problems. Uses in data mining and recommendation systems and for building adaptive user interfaces.
|32402D||048||Lecture||10:00-11:50am||Mon, Wed||10 of 37||Stefan Scherer||VKC260||PDF (166007 KB)|
|32410D||048||Lecture||3:30-6:50pm||Monday||34 of 37||Satish Thittamaranahalli Ka||KAP163||PDF (69171 KB)|
Data mining and machine learning algorithms for analyzing very large data sets. Emphasis on Map Reduce. Case studies. Recommended preparation: INF 550, INF 551 and INF 552. Knowledge of probability, linear algebra, basic programming, and machine learning.
|32403D||048||Lecture||6:00-7:50pm||Mon, Wed||39 of 50||Yao-Yi Chiang||GFS118||PDF (91241 KB)|
|32414D||048||Lecture||2:00-3:50pm||Mon, Wed||47 of 60||Wensheng Wu||SOSB4||PDF (154651 KB)|
Understand and apply user interface theory and techniques to design, build and test responsive applications that run on mobile devices and/or desktops. Recommended preparation: Knowledge of data management, machine learning, data mining, and data visualization.
The practice of User Experience Design and Strategy principles for the creation of unique and compelling digital products and services. Recommended preparation: Basic familiarity with web development and/or graphic design using a digital layout tool.
|32436R||048||Lecture||2:00-4:50pm||Monday||19 of 20||Jaime Levy||GFS111|
|32432R||048||Discussion||5:00-5:50pm||Monday||19 of 20||GFS111|
|32445R||048||Lecture||1:00-3:50pm||Friday||19 of 20||Jaime Levy||KAP159|
|32446R||048||Discussion||4:00-4:50pm||Friday||19 of 20||KAP159|
Function, design, and use of modern data management systems, including cloud; data management techniques; data modeling; network attached storage, clusters and data centers; relational databases; the map-reduce paradigm. Duplicates credit in INF 551. Recommended preparation: Basic understanding of engineering principles, including basic programming skills, knowledge of operating systems, networks, and databases; familiarity with the Python programming language is desired.
|32415D||048||Lecture||5:00-6:20pm||Mon, Wed||25 of 60||Wensheng Wu||KDC235||PDF (235356 KB)|
|32417D||048||Lecture||2:00-3:20pm||Mon, Wed||37 of 50||Carl Kesselman||LVL17|
Student teams working on external customer data analytic challenges; project/presentation based; real client data, and implementable solutions for delivery to actual stakeholders; capstone to degree. Recommended preparation: Knowledge of data management, machine learning, data mining, and data visualization.
|32429D||048||Lecture||3:30-6:50pm||Thursday||16 of 40||Atefeh Farzindar||WPH102||PDF (567418 KB)|
Picture archive communication system (PACS) design and implementation; clinical PACS-based imaging informatics; telemedicine/teleradiology; image content indexing, image data mining; grid computing in large-scale imaging informatics; image-assisted diagnosis, surgery and therapy.
- Crosslist: This course is offered by the BME department but may qualify for major credit in INF. To register, enroll in BME 528.
|29305D||034||Lecture||9:00-11:50am||Friday||4 of 10||Brent Liu||DEN@Viterbi|
|29310D||048||Lecture||9:00-11:50am||Friday||5 of 20||Brent Liu||OHE100C|
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): Informatics
|32448D||048||1.0-6.0||Lecture||TBA||TBA||16 of 30||Lizsl De Leon||OFFICE|