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Informatics (INF)
- 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
Fundamentals of data informatics: representation of data and knowledge, role of a data scientist, data storage/processing/analytics, machine learning, big data, and data visualization. Recommended preparation: A basic understanding of engineering and/or technology is recommended.
- Corequisite: ITP 115
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32445R | 001 | Lecture | 3:00-4:50pm | Mon, Wed | 11 of 20 | Sathyanaraya Raghavachary | THHB10 | PDF (440340 KB) |  |
Programming in Python for retrieving, searching, and analyzing data from the Web. Programming in Java. Learning to manipulate large data sets.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32424D | 048 | Lecture | 3:30-5:20pm | Tuesday | 33 of 40 | Jeremy Abramson | KAP158 | PDF (71631 KB) |  |
| 32440R | 048 | Lab | 3:30-5:20pm | Thursday | 33 of 40 | KAP145 | |
Threats to information systems; technical and procedural approaches to threat mitigation; policy specification and foundations of policy for secure systems; mechanisms for building secure security services; risk management. Recommended preparation: Background in computer security preferred. Recommended previous courses of study include computer science, electrical engineering, computer engineering, management information systems, and/or mathematics.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32428D | 048 | Lecture | 12:00-3:20pm | Friday | 16 of 25 | Tanya Ryutov | OHE136 | PDF (373544 KB) |  |
| 32429D | 034 | Lecture | 12:00-3:20pm | Friday | 9 of 12 | Tanya Ryutov | 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
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32442D | 048 | Lecture | 1:00-4:20pm | Friday | 12 of 30 | Clifford Neuman | OHE120 | PDF (128972 KB) | |
| 32426R | 048 | Discussion | TBA | TBA | 12 of 25 | OFFICE | | ||
| 32413D | 034 | Lecture | 1:00-4:20pm | Friday | 2 of 25 | Clifford Neuman | DEN@Viterbi | PDF (128972 KB) | |
| 32432R | 034 | Discussion | TBA | TBA | 2 of 25 | 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.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32448D | 048 | Lecture | 10:00-11:50am | Tue, Thu | 43 of 40 | Yolanda Gil,Gale Lucas | WPH207 | PDF (141250 KB) |  |
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.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32430D | 048 | Lecture | 3:30-5:20pm | Mon, Wed | 27 of 29 | Seon Kim | VKC151 | PDF (504080 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.
- Note: This course is offered through the Informatics Program and is NOT considered as CSCI credit for M.S. Computer Science students.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32418D | 048 | Lecture | 10:00-11:50am | Mon, Wed | 40 of 43 | Wensheng Wu | VPD106 | PDF (237178 KB) | |
| 32431D | 048 | Lecture | 3:30-5:20pm | Mon, Wed | 42 of 40 | Wensheng Wu | THH118 | PDF (237178 KB) | |
Practical applications of machine learning techniques to real-world problems. Uses in data mining and recommendation systems and for building adaptive user interfaces.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32427D | 048 | Lecture | 5:30-8:50pm | Tuesday | 48 of 49 | Ion Muslea | ZHS252 | PDF (136692 KB) | |
| 32450D | 048 | Lecture | 8:00-9:50am | Mon, Wed | 50 of 51 | Mohammad Reza Rajati | KDC235 | PDF (156467 KB) | |
Data mining and machine learning algorithms for analyzing very large data sets. Emphasis on Map Reduce. Case studies.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32423D | 048 | Lecture | 2:00-5:20pm | Friday | 68 of 70 | Rafael Ferreira Da Silva,Anoop Kumar | THH212 | PDF (123693 KB) | |
| 32443D | 048 | Lecture | 3:30-6:50pm | Tuesday | 45 of 50 | Wensheng Wu | WPHB27 | PDF (157755 KB) | |
| 32486D | 048 | Lecture | 2:00-3:50pm | Mon, Wed | 60 of 70 | Anna Farzindar | SLH100 | PDF (130531 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.
- Note: This course is offered through the Informatics Program and is NOT considered as CSCI credit for M.S. Computer Science students.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32421D | 048 | Lecture | 2:00-5:20pm | Wednesday | 59 of 61 | Luciano Nocera | SOSB4 | PDF (122366 KB) | |
The practice of User Experience Design and Strategy principles for the creation of unique and compelling digital products and services.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32409D | 048 | Lecture | 2:00-5:20pm | Monday | 25 of 25 | Jaime Levy | KAP134 | PDF (208275 KB) | |
| 32439R | 048 | Discussion | TBA | TBA | 25 of 25 | TBA | |
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.
- Prerequisite: 1 from (INF 551 or CSCI 585) and 1 from (INF 552 or CSCI 567)
- Note: This course is offered through the Informatics Program and is NOT considered as CSCI credit for M.S. Computer Science students.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32438D | 048 | Lecture | 2:00-5:20pm | Friday | 42 of 50 | Pedro Szekely,Jay Pujara | THH114 | PDF (180242 KB) | |
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.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 31529D | 048 | Lecture | 3:30-6:20pm | Friday | 40 of 40 | Cesar Acosta-Mejia | KAP156 | PDF (473686 KB) | |
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.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 29305D | 034 | Lecture | 9:00-11:50am | Friday | 4 of 20 | Brent Liu | DEN@Viterbi | | |
| 29310D | 048 | Lecture | 9:00-11:50am | Friday | 11 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): Informatics
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 32449D | 048 | Lecture | TBA | TBA | 8 of 22 | Lizsl De Leon | OFFICE | |
Course content to be selected each semester from recent developments in informatics.
| Section | Session | Units | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|---|
| Probability and Statistics for Data Science | ||||||||||
| 32451D | 048 | 4.0 | Lecture | 10:00-11:50am | Mon, Wed | 24 of 30 | Mohammad Reza Rajati | VHE206 | PDF (170066 KB) | |