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Course Type: Elective

Specialization: Foundations of Autonomous Systems

Instructor: Dr. Majid Zamani, Associate Professor

Prior knowledge needed:

This third course in the specialization focuses on modeling requirements. It is highly recommended that students take the first two courses that focus on the core structure in any autonomous system before attempting this course. We anticipate that students possess a grasp of fundamental mathematical concepts equivalent to those covered in the first year of studies for STEM majors at a US college. Additionally, a familiarity with basic differential equations and linear algebra is expected. This encompasses key principles, including: 

  • Sets and Functions: Proficiency in understanding the properties of sets, along with a clear comprehension of function definitions and their associated properties.

  • Eigenvalues and Eigenvectors: A basic knowledge of eigenvalues and eigenvectors of matrices, coupled with an ability to perform matrix-vector multiplication. 

  • Systems of First Order Linear Differential Equations:  A basic knowledge in solving and analyzing systems of first-order linear differential equations.

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Learning Outcomes

  • Master the use of linear temporal logic and automata to define system requirements.

  • Convert between regular expressions and non-deterministic finite automata (NFAs) for effective system requirements modeling.

  • Formulate essential system properties, such as safety and reachability.

  • Employ Linear Matrix Inequalities (LMIs) and Lyapunov functions to verify stability and robustness.

  • Calculate reachable sets and generate safety certificates using barrier functions.

  • Translate between omega-regular specifications and non-deterministic Büchi automata (NBAs).

  • Develop NBAs based on linear temporal logic formulas

Course Grading Policy


Percentage of Grade

6 Assignments

60% (10% each)

3 Quizzes

20% (6.7% each)

Final Exam




Course Content

Currently in development. Will be released when the course is launched. 


  • Cross-listed Courses: Courses that are offered under two or more programs. Considered equivalent when evaluating progress toward degree requirements. You may not earn credit for more than one version of a cross-listed course.
  • Page Updates: This page is periodically updated. Course information on the Coursera platform supersedes the information on this page. Click the View on Coursera button above for the most up-to-date information.