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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
Application of cryptography and cryptanalysis for information assurance in secure information systems. Classical and modern cryptography. Developing management solutions. Recommended preparation: Previous degree in computer science, mathematics, computer engineering, or informatics; understanding of number theory and programming background are helpful.
Assurance as the basis for believing an information system will behave as expected. Approaches to assurance for fielding secure information systems that are fit for purpose. Recommended preparation: Prior degree in computer science, electrical engineering, computer engineering, management information systems, and/or mathematics. Some background in computer security preferred.
|32423D||048||Lecture||6:40-9:20pm||Wednesday||22 of 25||Clifford Neuman||OHE120|
|32433D||034||Lecture||6:40-9:20pm||Wednesday||12 of 25||Clifford Neuman||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 522. Recommended preparation: Prior degree in computer science, mathematics, computer engineering, or informatics; advanced knowledge of computer architecture, operating systems, and communications networks will be valuable.
- Prerequisite: INF 522
|32416D||048||Lecture||5:00-7:20pm||Tuesday||17 of 30||Tanya Ryutov||KAP146|
Preservation, identification, extraction and documentation of computer evidence stored on a computer. Data recovery; cryptography; types of attacks; steganography; network forensics and surveillance. Recommended preparation: Previous degree in computer science, mathematics, computer engineering, or informatics; a working understanding of number theory and some programming knowledge will be helpful.
|32408D||048||Lecture||12:30-1:50pm||Tue, Thu||15 of 30||Joseph Greenfield||OHE120|
|32438D||034||Lecture||12:30-1:50pm||Tue, Thu||10 of 12||Joseph Greenfield||DEN@Viterbi|
Fundamental concepts in information security and privacy; Security and privacy policies, threats, and protection mechanisms; Security and privacy laws, regulations, and ethics.
|32412D||048||Lecture||12:00-2:50pm||Friday||14 of 40||Clifford Neuman||OHE100C|
|32413D||034||Lecture||12:00-2:50pm||Friday||8 of 10||Clifford Neuman||DEN@Viterbi|
Introduction to data analysis techniques and associated computing concepts for non-programmers. Topics include foundations for data analysis, visualization, parallel processing, metadata, provenance, and data stewardship. Recommended preparation: Mathematics and logic undergraduate courses.
|32406D||048||Lecture||11:00-12:20pm||Tue, Thu||11 of 25||Yolanda Gil||SOSB41||PDF (182426 KB)|
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. Recommended preparation: INF 550 taken previously or concurrently; understanding of operating systems, networks, and databases; experience with probability, statistics, and programming.
|Seon Kim||PDF (135967 KB)|
|32411D||048||Lecture||8:30-9:50am||Mon, Wed||23 of 50||Wensheng Wu||GFS116||PDF (198089 KB)|
Practical applications of machine learning techniques to real-world problems. Uses in data mining and recommendation systems and for building adaptive user interfaces. Recommended preparation: INF 550 and INF 551 taken previously or concurrently; knowledge of statistics and linear algebra; programming experience.
|32402D||048||Lecture||2:00-4:50pm||Wednesday||34 of 37||Stefan Scherer||WPH207||PDF (86884 KB)|
|32410D||048||Lecture||3:30-6:20pm||Thursday||34 of 37||Satish Thittamaranahalli Ka||KAP146|
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||11:00-12:20pm||Tue, Thu||10 of 42||Wensheng Wu||GFS207||PDF (168795 KB)|
|32414D||048||Lecture||10:00-11:50am||Mon, Wed||31 of 40||Yao-Yi Chiang,Atefeh Farzindar||KAP158||PDF (111236 KB)|
|32426D||048||Lecture||2:00-3:20pm||Tue, Thu||13 of 40||Wensheng Wu||VHE217||PDF (168795 KB)|
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.
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.
|32427D||048||Lecture||5:00-6:20pm||Mon, Wed||27 of 30||Amir Soheili||GFS222|
The practice of User Experience Design and Strategy principles for the creation of unique and compelling digital products and services.
|32428R||048||Lecture||5:00-7:50pm||Monday||19 of 20||Jaime Levy||VKC161||PDF (175593 KB)|
|32436R||048||Lecture||2:00-4:50pm||Monday||19 of 20||Jaime Levy||VKC161||PDF (175593 KB)|
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||3:00-5:50pm||Friday||8 of 40||Atefeh Farzindar||KAP163||PDF (411325 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. Prerequisite: BME 425 or BME 525 and BME 527.
- Prerequisite: 1 from (BME 425 or BME 525) and BME 527
- Crosslist: This course is offered by the BME department but may qualify for major credit in INF. To register, enroll in BME 528.
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
|32448D||048||1.0-6.0||Lecture||TBA||TBA||17 of 30||Art Perez||OFFICE|