SAE Courses

2025-2026 Master’s SAE program catalogue (requirements)
- Fall 2025 admitted students and onwards
2024-2025 Master’s SAE program catalogue (requirements)
- Students admitted prior to Fall 2025
2025-2026 Graduate Certificate SAE program catalogue (requirements)

For the courses offered during any given semester, consult the Schedule of Classes.

For the list of qualified elective courses, consult the latest USC Catalogue.

Required Courses

This course, the first course all students must take in their first semester, introduces students to the key concepts and heuristics employed in developing systems architectures for aerospace, defense, automotive, and manufacturing systems. It focuses on both the conceptual and acceptance phases. The course emphasizes both synthetic (i.e., integrative) and analytic methods in problem formulation and problem-solving. Students learn to formulate the right problem (resist oversimplification to fit a known technique) and see the “big picture” in terms of program & system scope (not individual subsystems). Students are introduced to architectural frameworks, trade-off analysis, the role of ontology engineering in systems architecting, systems thinking, the use of heuristics in systems architecting, and architecture-aware human-system integration concepts. Modeling, simulation, and prototyping concepts are presented in the context of systems architecting. Real-world case studies are presented, with specific emphasis on the role of system architects and their relationship to systems engineers and other members of the project teams. Concepts from transdisciplinary systems engineering are presented, along with how they can enhance systems architecting.

Course Description: Systems engineering is essential to achieving success for systems products, services, and processes across Aerospace & Defense, Transportation, Medical, and Energy Management. As such, systems become increasingly complicated and complex, fundamental concepts and rigor become increasingly important throughout the system life cycle: from concept evaluation through retirement. This class provides both theoretical and practical knowledge needed for conceptualizing, designing, supporting, and evaluating today’s and tomorrow’s systems.

Learning Objectives:

Introduce students to Systems Engineering support of design processes, architecture concepts, operations concepts, systems integration, and life-cycle support concepts.
Explore means for performing trade studies and evaluating risk.
Introduce system Verification, Validation, Quality, Test, Specialty Engineering, Security,
and Mission Assurance concepts.
Discuss representative systems that highlight course concepts.

The course focused on probability theory and its applications in system testing and performance evaluation, test design, assessment of test accuracy, and fidelity. It also covers reliability, maintainability, and quantitative decision models in systems engineering. This course provides principles and methods of Constraint theory to manage and de-conflict complex requirements. Complexity Theory is covered with applications to software-intensive and complex systems. Upon successful completion of this course, a student should be able to demonstrate analytical skills in applying quantitative methodologies in critical consideration and performance of various systems engineering activities.

This course provides a deep understanding of Model-Based approaches in systems architecture and engineering. Students will be exposed to modeling system requirements, structure, behavior, and parametric relationships. The course covers the mapping of models to hardware description languages and presents code generation concepts at the hardware level. Students are introduced to key concepts such as ontologies and metamodels and how they can be exploited in MBSE. Students learn to model systems using software and modeling language such as SySML. Students are taught methods to assess whether an organization is prepared to undertake a transformation to MBSE, as well as how to perform economic analysis to determine the potential benefits (or not) of MBSE for an organization.

Course Introduction and Purpose:

With the increasing scale and complexity of systems and the need for systems to perform their missions and participate in larger system-of-systems to achieve more complex missions, systems integration and system-of-systems integration have become key areas of emphasis in aerospace, defense, telecommunications, transportation, and emergency services engineering and research. The terms “system integration” and “SOS integration” can mean many things to many people. This course emphasizes the importance of stakeholder concerns and integration contexts before discussing theories, methods, processes, and tools. The course presents SI’s key perspectives and challenges, case studies, and examples from several aerospace and government programs to reinforce the principles. The course discusses legacy integration, human-system integration, SOS integration, interoperability, and software integration challenges. Materials include theory, real-world case studies, and findings from the recent literature.

Course Purpose: Artificial Intelligence and Machine Learning are increasingly important aspects of everyday life and offer significant opportunities to improve engineering practices. As engineers, our job is to understand the advantages and limitations of AI and ML so that we can interpret and use their results accurately. The purpose of this class is to introduce key concepts and challenge students to understand where and when to use and trust AI and ML.

Course Description: Review of probability and statistics, linear modeling, hypothesis testing, knowledge representation and supervised and unsupervised learning, optimization, evolutionary algorithms; augmented intelligence in decision-making and intelligent monitoring and control; real-world applications.

Learning Objectives: After taking this course, students will be able to -

Explain using AI and ML techniques for systems engineering.
Use AI and ML methods in the design and optimization of complex systems.
Understand the application of Large Language Models (LLMs) in systems engineering.

SAE Electives

The design and development of major engineering systems are invariably influenced political processes in governments. These processes often determine the approval and funding of programs. System architects tend to be trained in techniques to deal with cost, schedule, and performance challenges, but rarely if ever in understanding the role of government politics on their programs and projects. This class teaches students in methods to mitigate political risk and successfully navigate political processes that directly or indirectly affect engineering decisions. Real-world case studies are used to communicate the impact of political processes, analyze them to reveal potential risks on systems architecting, and teach political risk mitigation techniques. Students will learn the various ways that political processes influence the architectures of government systems. Students will learn about the U.S. Federal Government acquisition processes (Administrative and Congressional) and typical funding and approval procedures employed on major government-funded systems programs. The students will learn how to to anticipate the impacts of political decisions through application of risk analysis and heuristics.

The course presents the different definitions and interpretations of resilience and provides examples from various domains. It presents practical formal methods for the design and analysis of resilient systems and system-of-systems (SoS). These include deterministic models, probabilistic models, and hybrid models. It presents various disruption types, and risks as a function of system scale and complexity. Architectural implications and management strategies for resilient SoS are also discussed in this class. Guest lectures by industry practitioners supplement theory with real-world practical examples. Illustrative examples covered in guest lectures include UAV swarm operations and self-driving vehicle networks.

This course is intended to give systems architects and engineers key insight into the impact of economic factors on their programs and help them in the use of tools to gain an in-depth understanding of these factors. The fundamental terms involved in quantitative analysis of cash flow are covered. Insight into cost estimation techniques are also shared. The relationship of software and its cost implications are also presented. Building on the work that has been done in software cost modeling, the course examines how these methods can be applied to systems engineering. This course is intended to give students a strong foundation in time value of money, interest rate concepts, and practical aspects of economic justification of projects in an organization. A key goal of the course is to improve students’ ability to create and understand parametric cost models, and learn to perform risk analysis and trade studies using models.