Carnegie Mellon University

Carnegie Mellon Institute for Strategy & Technology

CMU's Home for Political Science and International Relations

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The area of concentration provides an opportunity to build upon core course knowledge and to develop expertise in a specific area.  Students choose a focus area (24 units) from the following concentration areas:

  • Data Analytics
  • Politics and Strategy
  • Information Security
  • Software and Networked Systems

Students can further explore their area of concentration, or pursue topics outside of it, through free electives. At least one course (12 units) must be taken from outside the student's focus area.

Students may choose courses for their area of concentration and free electives from amongst the following:

Concentration Courses 

Data Analytics (DA)

This concentration focuses on the mastery of techniques such as: machine learning, social network analysis, large-scale data reduction and filtering and the application of these techniques to the development of strategies to advance the organizational mission.

  • 05-820 Social Web (spring)
  • 10-605 Machine Learning Large Data Sets (fall/spring, prereq 10-601 or 10-701)
  • 10-606 Mathematical Foundations for Machine Learning (fall 1st half mini)
  • 10-607 Computational Foundations for Machine Learning (fall 2nd half mini)
  • 10-623 Generative AI (spring, prerequsite 10-601 or 10-701)
  • 10-703 Deep Reinforcement Learning and Control (fall, prereq 10-601 or 10-701)
  • 10-707 Topics in Deep Learning (spring, prereq 10-601 or 10-701)
  • 10-708 Probabilistic Graphics Models (fall/spring, prereq 10-601 or 10-701)
  • 10-613/10-713 Machine Learning Ethics and Society (spring, prereq 10-601 or 10-701)
  • 10-714 Deep Learning Systems: Algorithms & Implementation (fall, prereq 15-513 AND 10-601/701)
  • 10-718 Machine Learning in Practice (fall/spring)
  • 11-611/711 Natural Language Processing (fall/spring)
  • 11-641 Machine Learning for Text Mining (fall/spring)
  • 11-642 Search Engines (fall/spring)
  • 11-685 Introduction to Deep Learning (fall/spring)
  • 11-731 Machine Translation and Sequence-to-Sequence Models (fall)
  • 11-747 Neural Networks for NLP (spring)
  • 11-777 Multimodal Machine Learning (fall/spring, prereq 10-601 or 10-701)
  • 11-868 Large Language Model Systems (spring, prereq 11-711 or 11-785)
  • 15-688 Practical Data Science (spring)
  • 15-780 Graduate Artificial Intelligence (spring)
  • 16-735 Ethics and Robotics (spring)
  • 16-824 Visual Learning and Recognition (fall/spring)
  • 17-634 Applied Machine Learning (spring 1st half mini)
  • 17-644 Applied Deep Learning (spring 2nd half mini) 
  • 17-645/17-745/11-695 Machine Learning in Production/AI Engineering (fall/spring)
  • 17-685/17-801 Dynamic Network Analysis (spring)


Politics and Strategy (PS)

This concentration focuses on developing a sound reasoning framework and the critical thinking skills necessary to develop effective policies and strategies for those decision makers who will shape the future of their technology intensive organizations.

  • 84-600 Security War Game Simulation (fall 1st half mini)
  • 84-619 Civil-Military Relations (spring/summer)
  • 84-622 Nonviolent Conflict and Revolution (spring)
  • 84-624 Future of Democracy (spring)
  • 84-625 Contemporary American Foreign Policy (spring)
  • 84-628 Military Strategy and Doctrine (fall)
  • 84-649 Digital Diplomacy: Cybersecurity Challenges and Global Governance (spring)
  • 84-662 Diplomacy and Statecraft (fall)
  • 84-663 Click. Hack. Rule: Understanding the Power and Peril of Cyber Conflict (fall)
  • 84-665 The Politics of Fake News and Misinformation (spring)
  • 84-669 Decision Science for International Relations (fall)
  • 84-671 International Governance of Artificial Intelligence (fall)
  • 84-672 Space and National Security (spring)
  • 84-673 Emerging Technologies and International Law (fall)
  • 84-680 US Grand Strategy (fall)
  • 84-683 Cyber Policy as National Policy (spring 2nd half mini)
  • 84-686 The Privatization of Force (fall)
  • 84-688 Concepts of War and Cyber War (fall 1st half mini)
  • 84-689 Terrorism and Insurgency (spring)
  • 84-690 Social Media, Technology, and Conflict (spring)
  • 84-720 International Security Graduate Seminar (spring)
  • 16-735 Ethics and Robotics (spring)
  • 17-652 Innovation and Entrepreneurship in Technology (summer 2nd half mini)
  • 17-684 Ethics and Policy Issues in Computing (fall/spring)
  • 19-701 Introduction to the Theory & Practice of Policy (fall)
  • 19-711 Science and Innovation Leadership for the 21st Century: Firms, Nations, and Tech (spring)
  • 19-713 Policies of Wireless Systems (fall)
  • 19-722 Telecommunications Technology and Policy for the Internet Age (spring)


Information Security (IS)

This concentration focuses on developing an understanding of cyber-attacks and equipping students to address the threats and consequences of cyber-attacks through sound information security strategies and policies.

  • 05-836 Usable Privacy and Security (spring)
  • 14-735 Secure coding (fall, prerequisite 18-613)
  • 14-761 Applied Information Assurance (fall/spring)
  • 14-814/18-637 Wireless Security (spring, prerequisites 18-631/14-741 or 18-730 AND 18-741 or 15-641)
  • 14-817 Cyber Risk Modeling (fall/spring)
  • 14-819 Introduction to Software Reverse-Engineering (spring, prerequisite 15-513)
  • 14-822 Host Based Forensics (spring, co-requisite 14-761)
  • 14-823 Network Forensics (fall, co-requisite 14-761)
  • 18-731 Network Security (fall/spring, prerequisite 18-631/14-741 or 18-730)
  • 18-732 Secure Software Systems (spring, prerequisite 18-631/14-741 and 15-513)
  • 18-733 Applied Cryptography (spring, prerequisite 18-631/14-741 or 18-730)
  • 18-734 Foundation of Privacy (fall)
  • 84-683 Cyber Policy as National Policy (spring)
  • 95-810 Blockchain Fundamentals (fall 1st half mini/summer)
  • 95-855 Network Traffic Analysis (fall 2nd half mini/summer 2nd half mini)
  • 95-884 Network Defenses (fall 1st half mini/spring 1st half mini/summer 1st half mini)


Software and Networked Systems (SNS)

This concentration focuses on developing an understanding of systems and software comprising organizational information infrastructure assets to better manage their design, development, and procurement of systems and service, through sound strategies and policies.

  • 11-642 Search Engines (fall/spring)
  • 14-740 Fundamentals of Telecommunication Networks (fall/spring)
  • 14-848 Cloud Infrastructure: Design, Analysis and Implementation (fall/spring)
  • 15-618 Parallel Computer Architecture and Programming (fall/spring)
  • 15-619/15-719/18-709 Cloud Computing (fall/spring)/Advanced Cloud Computing (spring, prerequisite 15-513)
  • 15-746 Storage Systems (fall, prerequisite 15-513)
  • 16-720 Computer Vision (fall/spring)
  • 16-722 Sensing and Sensors (fall)
  • 16-761 Mobile Robots (spring)
  • 16-782 Planning and Decision-making in Robotics (fall)
  • 17-610 Risk Management for Software Intensive Projects (spring 2nd half mini/summer 2nd half mini)
  • 17-611 Statistics for Decision Making (fall 1st half mini/summer 2nd half mini) AND 17-626 Requirements for Information Systems (fall 2nd half mini) OR 17-627 Requirements for Embedded Systems (fall 2nd half mini)
  • 17-614 Formal Methods (fall 1st half mini) AND 17-622 Agile Methods (fall 2nd half mini/summer 1st half mini) OR 17-624 Advanced Formal Methods (fall 2nd half mini)
  • 17-616 DevOps: Engineering for Deployment and Operations (fall)
  • 17-623 Quality Assurance (fall 2nd half mini)
  • 17-642 Software Management Theory (spring 2nd half mini) AND 17-643 Quality Management (spring 2nd half mini, prerequisite 17-623)
  • 17-645/17-745/11-695 Machine Learning in Production (fall/spring) /AI Engineering (spring, prerequisite 10-600)
  • 17-648 Sensor Based Systems (spring 2nd half mini, prerequisite 17-636)
  • 17-652 Innovation and Entrepreneurship in Technology (summer 2nd half mini)
  • 17-681 Java for Application Programmers (fall 1st half mini/spring 1st half mini) AND 17-683 Data Structures for Application Programmers (fall 2nd half mini/spring 1st and 2nd half mini)
  • 18-642 Embedded System Software Engineering (fall)
  • 18-648 Embedded Real Time Systems (fall, prerequisite 15-513)
  • 18-756 Packet Switching & Computer Networks (fall)
  • 18-843 Mobile and Pervasive Computing (fall)


Electives (12 units) 

An elective is a course taken in addition to the core, capstone project, pre-requisite and required courses, is in an area of interest to the student, and is applicable to their degree. The most common choice is to select outside of the student’s declared concentration, from the pre-approved list of concentration courses. MITS students should plan to select technical courses, although some exceptions to this rule will be considered. 

All MITS students are required to take a minimum of 12 units of elective coursework. Units associated with elective courses vary. All students are advised to discuss their choice of electives with the Graduate Program Manager and receive approval before enrolling. If a student wishes to take an elective outside of the pre-approved courses, only those electives that have been approved by the academic adviser will be accepted towards degree completion requirements.

Summer Internship for MITS-Applied Study (3 units)

MITS – Applied Study students are required to enroll in 3 units of the Pass/No Pass internship course during the summer semester of their internship. To count towards the degree students must work with their internship supervisor to submit expectations and outcomes, and the students shall submit a final report at the conclusion of the internship.