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Course Type: Elective

Specialization: Foundations of Autonomous Systems

Instructor: Dr. Majid Zamani, Associate Professor

Prior knowledge needed:

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

  • Model basic autonomous systems including linear control.
  • Describe solutions and behaviors of systems sequential circuits, and simple timed automatations in a unified fashion.
  • Define and illustrate interconnections between systems. 
  • Recognize the prevalence of autonomous systems in sectors like avionics, automotive, robotics, medical devices, and more. 

Course Grading Policy


Percentage of Grade

6 Assignments

60% (10% each)

3 Quizzes

20% (6.7% each)

Final Exam




Course Content

Duration: 3 hours

In this introductory module, we delve into the world of autonomous and cyber-physical systems, their significance, structure, and applications. By studying real-world examples, such as the Ariane 5 rocket failure, adaptive cruise control, and self-driving cars, we will grasp the foundational understanding of the importance of modeling in autonomous systems. Moreover, we'll discuss key components of these systems, the tight interaction between hardware and software, and the ubiquity of autonomous systems in various sectors.

Duration: 2 hours

In Module 2, we delve into the nuances of system modeling. Through instructional videos, students grapple with system definitions, state diagrams, and transition systems. Relevant assignments further solidify this knowledge. Real-world examples, like the Northeast Blackout of 2003, underscore the importance of precise modeling, while practical systems such as a Beverage Vending Machine and Turnstile illustrate core concepts. A truly academic journey into the essence of system modeling awaits.

Duration: 3 hours

Module 3 introduces students to the fundamental principles of modeling dynamic systems, focusing on translational mechanical systems, rotational mechanical systems, and analog circuits. Emphasizing the mathematical relationships underlying these systems, the course progresses into more specific examples and dives deep into timed and hybrid automata, providing a comprehensive understanding of the role of timing in systems modeling.

Duration: 3 hours

Module 4 dives deep into understanding system solutions, behaviors, and various compositions. Learners will be introduced to the mathematical representations of systems and their behaviors. Through a series of engaging video content, learners will explore parallel, serial, and feedback compositions. Additionally, the module provides practical assignments to enhance comprehension and a detailed study of system modeling.

Duration: 3 hours

This module contains materials for the final exam for MS-CS degree students. If you have upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials (quiz) in the previous module and anywhere else they may be found. 


  • 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.
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