Carnegie Mellon University
Software and Societal Systems Department  ›  Societal Computing PhD Program  ›  Program  ›  Courses in Societal Computing

Courses in Societal Computing

Interested in taking a course in Societal Computing? Here you will find a listing of Societal Computing courses taught within the program here at Carnegie Mellon. Please note that not all courses are taught every semester, so it is important to check The Hub before making any future course of study plans. 

Important Notes

Students are welcome to take electives outside of these lists.

To learn more about courses and program policies, take a look at our SC Student Handbook.

The Societal Computing Ph.D. program has a curriculum designed to make sure that students have a strong foundation in the fundamentals of the field and opportunities for specialization.

The curriculum includes a variety of courses:

  • Practicum: During their first year, students take a practicum that introduces them to a variety of topics and skills in Societal Computing, including giving presentations, collaborating, and preparing an IRB application.
  • Core Skills Courses: The curriculum includes courses in four key skill areas: Societal Computing, Computational Thinking, Statistics, and Management and Policy. These courses give students a foundation in skills such as algorithm design, policy analysis, and statistical data analysis.
  • Electives: Students also take a number of elective courses. These electives allow students to focus on a specific area within Societal Computing. Students can take electives in areas like machine learning, privacy, and security, or they can design an independent study on a topic that isn't covered in the existing courses. Students may also be able to take elective courses outside of the Societal Computing program in other departments at CMU or at the University of Pittsburgh.

Students also have the opportunity to participate in research throughout the program. During their first two years, students should expect to work on research for at least half of their time. After they complete their coursework, students should be doing research full time.

For more details on course requirements, please see the SC Student Handbook and for more information on CMU course offerings, visit the Hub.

Core Skills Courses

Societal Computing

  • 17- PhD level (or masters with permission of instructor) course taught by Core SC faculty
  • 10-713 Machine Learning, Ethics, and Society
  • 10-735 Responsible AI
  • 17-703 Cryptocurrencies, Blockchains, and Applications
  • 17-722 Building User-Focused Sensing Systems
  • 17-733 Privacy, Policy, Law, and Technology
  • 17-734 Usable Privacy and Security
  • 17-735 Engineering Privacy in Software
  • 17-737 Artificial Intelligence Methods for Social Good
  • 17-759 Advanced Topics in Machine Learning & Game Theory
  • 17-781 Mobile & Pervasive Computing Services
  • 17-801 Dynamic Network Analysis
  • 17-803 Empirical Methods
  • 17-821 Computational Modeling of Complex Socio-Technical Systems
  • 17-840 Green Computing

Computational Thinking Skills

Computational Thinking skills courses address issues of how to reason computationally. These courses involve the design and development of core algorithms and not just the application of canned programs.

  • 10-601/10-701/15-781 Machine Learning
  • 10-715 Advanced Introduction to Machine Learning
  • 11-711 Algorithms for NLP
  • 11-785 Introduction to Deep Learning
  • 14-741/18-631 Introduction to Information Security
  • 15-750 Algorithms
  • 15-780 Advanced AI Concepts
  • 15-830 Computational Methods in Sustainable Energy
  • 15-853 Algorithms in the Real World
  • 15-892 Foundations of Electronic Marketplaces
  • 17-731/18-734 Foundations of Privacy
  • 17-737 Artificial Intelligence Methods for Social Good
  • 17-759 Advanced Topics in Machine Learning and Game Theory
  • 17-821 Computer Simulation of Complex Socio-Technical Systems
  • 17-880 Algorithms for Private Data Analysis
  • 18-730 Introduction to Computer Security

Policy and Management

These courses address issues of management and policy. Methods courses are not allowed in this area.

  • 17-762 Law of Computer Technology
  • 17-733 Privacy Policy, Law, and Technology
  • 19-701 Theory and Practice of Policy Analysis
  • 19-702 Quantitative Methods for Policy Analysis
  • 19-705 Workshop on Applied Policy Analysis (6 units)
  • 19-712/18-842 Telecommunications Technology, Policy and Management
  • 19-713 Policies of Wireless Systems
  • 19-718 Public Policy and Regulations
  • 47-890 Seminar in Organizational Behavior
  • 47-891 Seminar in Organizational Theory (6 units)
  • 90-840 Legislative Policy Making

Statistics

These courses address issues of statistical data analysis, and provide methodological skill in statistics.

  • 10-708 Probabilistic Graphical Models
  • 10-716 Advanced Machine Learning: Theory and Methods
  • 19-703 Applied Data Analysis I (6 Units)
  • 19-704 Applied Data Analysis II (6 Units)
  • 36-700 Probability and Mathematical Statistics
  • 36-705 Intermediate Statistics
  • 36-707 Regression Analysis
  • 36-749 Experimental Design for Behavioral & Social Sciences
  • 90-906 Intro Econometric Theory
  • 94-834 Applied Econometrics I
  • 94-835 Applied Econometrics II