Southern Utah University

Course Syllabus

Southern Utah University
Southern Utah University
Spring Semester 2026

Introduction to AI and Machine Learning for Engineers (Face-to-Face)

ME 3600-01

Course: ME 3600-01
Credits: 3
Term: Spring Semester 2026
Department: ET
CRN: 10887

Course Description

In this course, students will explore the world of Machine Learning (ML), a fundamental branch of Artificial Intelligence (AI), and explore its real-world applications. This undergraduate course welcomes students from diverse backgrounds, including, but not limited to, engineering, computer science, and mathematics, providing hands-on experience and practical skills. Students will be introduced to ML's core principles and terminology and learn to apply ML techniques to engineering and technology challenges. Additionally, students will have the opportunity to collaborate on projects that make a meaningful difference. This course will help students unlock the potential of Machine Learning and become a leader in this dynamic field. (Fall) [Graded (Standard Letter)] Prerequisite(s): (MATH 1210 or MATH 1040) and (ENGR 2170 or CS 1400) - Prerequisite Min Grade: D-

Required Texts

Recommended Texts: Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Learning Outcomes

  1. Core ML Knowledge: Gain a solid understanding of fundamental Machine Learning principles, terminologies, and concepts.
  2. Practical Skills: Develop hands-on expertise in applying ML techniques to solve real-world engineering problems. 
  3. Collaboration: Collaborate effectively in multidisciplinary teams, harnessing the power of diverse skills to tackle complex challenges.
  4. Problem-Solving: Acquire the ability to analyze data, create predictive models, and optimize processes, enhancing decision-making in engineering and related fields.
  5. Industry Relevance: Explore how ML is used in industry and gain insights into its practical applications and potential career paths.
  6. Programming and Data Analysis: Build a foundation in programming and data analysis, essential skills for success in the ML field.

Course Requirements

Prerequisites:
Students must have completed MATH 1210 or MATH 1040, and ENGR 2170 or CS 1400 prior to enrolling in this course. 

Course Outline

Course Outline:
This course begins with an introduction to machine learning concepts and a review of Python programming for data analysis. Topics include supervised and unsupervised learning methods such as linear, polynomial, nonlinear, and multiple regression; classification techniques including K-Nearest Neighbors, Support Vector Machines, and logistic regression; clustering using K-Means; and artificial neural networks. Students will gain hands-on experience through in-class lab assignments and apply learned techniques to real-world engineering problems, culminating in a final project and presentation. 

Instructor's policies on late assignments and/or makeup work

 Homework assignments must be submitted by the posted deadline. Late homework may not be accepted unless prior approval is granted by the instructor due to documented extenuating circumstances. Lab assignments must be completed in class and as scheduled; no late or makeup lab work is permitted. Exams must be taken at the scheduled time. Makeup exams will only be allowed with advance notice and valid documentation. Unexcused absences will result in a score of zero. 

Attendance Policy

 Regular attendance is required and expected. Students are responsible for all material covered during class sessions, including lectures and in-class lab activities. Absences do not excuse missed work or assignments. If a student is ill or has an approved university-related obligation, they should notify the instructor as soon as possible to discuss reasonable accommodations. Excessive absences may negatively impact student performance and course success. 

Course Fees

Content for this section will be provided by the instructor.

ADA Statement

Students with medical, psychological, learning, or other disabilities desiring academic adjustments, accommodations, or auxiliary aids will need to contact the Disability Resource Center, located in Room 206F of the Sharwan Smith Center or by phone at (435) 865-8042. The Disability Resource Center determines eligibility for and authorizes the provision of services.

If your instructor requires attendance, you may need to seek an ADA accommodation to request an exception to this attendance policy. Please contact the Disability Resource Center to determine what, if any, ADA accommodations are reasonable and appropriate.

Academic Credit

According to the federal definition of a Carnegie credit hour: A credit hour of work is the equivalent of approximately 60 minutes of class time or independent study work. A minimum of 45 hours of work by each student is required for each unit of credit. Credit is earned only when course requirements are met. One (1) credit hour is equivalent to 15 contact hours of lecture, discussion, testing, evaluation, or seminar, as well as 30 hours of student homework. An equivalent amount of work is expected for laboratory work, internships, practica, studio, and other academic work leading to the awarding of credit hours. Credit granted for individual courses, labs, or studio classes ranges from 0.5 to 15 credit hours per semester.

Academic Freedom

SUU is operated for the common good of the greater community it serves. The common good depends upon the free search for truth and its free exposition. Academic Freedom is the right of faculty to study, discuss, investigate, teach, and publish. Academic Freedom is essential to these purposes and applies to both teaching and research.

Academic Freedom in the realm of teaching is fundamental for the protection of the rights of the faculty member and of you, the student, with respect to the free pursuit of learning and discovery. Faculty members possess the right to full freedom in the classroom in discussing their subjects. They may present any controversial material relevant to their courses and their intended learning outcomes, but they shall take care not to introduce into their teaching controversial materials which have no relation to the subject being taught or the intended learning outcomes for the course.

As such, students enrolled in any course at SUU may encounter topics, perspectives, and ideas that are unfamiliar or controversial, with the educational intent of providing a meaningful learning environment that fosters your growth and development. These parameters related to Academic Freedom are included in SUU Policy 6.6.

Academic Misconduct

Scholastic honesty is expected of all students. Dishonesty will not be tolerated and will be prosecuted to the fullest extent (see SUU Policy 6.33). You are expected to have read and understood the current SUU student conduct code (SUU Policy 11.2) regarding student responsibilities and rights, the intellectual property policy (SUU Policy 5.52), information about procedures, and what constitutes acceptable behavior.

Please Note: The use of websites or services that sell essays is a violation of these policies; likewise, the use of websites or services that provide answers to assignments, quizzes, or tests is also a violation of these policies. Regarding the use of Generative Artificial Intelligence (AI), you should check with your individual course instructor.

Emergency Management Statement

In case of an emergency, the University's Emergency Notification System (ENS) will be activated. Students are encouraged to maintain updated contact information using the link on the homepage of the mySUU portal. In addition, students are encouraged to familiarize themselves with the Emergency Response Protocols posted in each classroom. Detailed information about the University's emergency management plan can be found at https://www.suu.edu/emergency.

HEOA Compliance Statement

For a full set of Higher Education Opportunity Act (HEOA) compliance statements, please visit https://www.suu.edu/heoa. The sharing of copyrighted material through peer-to-peer (P2P) file sharing, except as provided under U.S. copyright law, is prohibited by law; additional information can be found at https://my.suu.edu/help/article/1096/heoa-compliance-plan.

You are also expected to comply with policies regarding intellectual property (SUU Policy 5.52) and copyright (SUU Policy 5.54).

Mandatory Reporting

University policy (SUU Policy 5.60) requires instructors to report disclosures received from students that indicate they have been subjected to sexual misconduct/harassment. The University defines sexual harassment consistent with Federal Regulations (34 C.F.R. Part 106, Subpart D) to include quid pro quo, hostile environment harassment, sexual assault, dating violence, domestic violence, and stalking. When students communicate this information to an instructor in-person, by email, or within writing assignments, the instructor will report that to the Title IX Coordinator to ensure students receive support from the Title IX Office. A reporting form is available at https://cm.maxient.com/reportingform.php?SouthernUtahUniv

Non-Discrimination Statement

SUU is committed to fostering an inclusive community of lifelong learners and believes our university's encompassing of different views, beliefs, and identities makes us stronger, more innovative, and better prepared for the global society.

SUU does not discriminate on the basis of race, religion, color, national origin, citizenship, sex (including sex discrimination and sexual harassment), sexual orientation, gender identity, age, ancestry, disability status, pregnancy, pregnancy-related conditions, genetic information, military status, veteran status, or other bases protected by applicable law in employment, treatment, admission, access to educational programs and activities, or other University benefits or services.

SUU strives to cultivate a campus environment that encourages freedom of expression from diverse viewpoints. We encourage all to dialogue within a spirit of respect, civility, and decency.

For additional information on non-discrimination, please see SUU Policy 5.27 and/or visit https://www.suu.edu/nondiscrimination.

Pregnancy

Students who are or become pregnant during this course may receive reasonable modifications to facilitate continued access and participation in the course. Pregnancy and related conditions are broadly defined to include pregnancy, childbirth, termination of pregnancy, lactation, related medical conditions, and recovery. To obtain reasonable modifications, please make a request to title9@suu.edu. To learn more visit: https://www.suu.edu/titleix/pregnancy.html.

Disclaimer Statement

Information contained in this syllabus, other than the grading, late assignments, makeup work, and attendance policies, may be subject to change with advance notice, as deemed appropriate by the instructor.