Machine Learning Course Syllabus

ChE 426: Machine Learning for Engineers (3 credit hours).

Theory of machine learning with engineering applications.

Machine learning is a convergence of linear algebra, statistics, optimization, and computational methods to allow computers to make decisions and take action from data. Examples of machine learning are now pervasive and are expected to further influence transportation, entertainment, retail, and energy industries. This engineering course reviews theory and applications of machine learning to engineering applications with a survey of unsupervised and supervised learning methods.

The course combines mathematical details with several case studies that provide an intuition for machine learning with methods for classification, regression, and dimensionality reduction. A second phase of the course is a hands-on group project. The engineering problems and theory will guide the student towards a working fluency in state-of-the-art methods implemented in Python.

 Machine Learning for Engineers
 Blended Online and Classroom Learning
 MWF - 11:00-11:50 am, 393 CB

Learning Outcomes

  • Visualize data to understand relationships and assess data quality
  • Apply linear algebra, statistics, and optimization techniques to create machine learning algorithms
  • Understand engineering and business objectives to plan applications
  • Assess data information content and predictive capability
  • Detect overfitting and implement strategies to improve prediction
  • Master the use of machine learning packages with understanding of how hyperparameters can be adjusted to improve performance
  • Understand the differences between classification, regression, and clustering and when each can be applied
  • Communicate machine learning decisions with uncertainty quantification
  • Implement machine learning techniques successfully to complete a group project

Recitation Sessions

As needed through-out the semester. The Teaching Assistants will conduct the recitation sessions. Generally they will be held:

  • Before exams
  • To help work through difficult project issues
  • For additional class time


  • Graded Items
    • Homework / Quizzes = 20%
    • Mid-Term Exam = 25%
    • Final Exam = 25%
    • Project Written and Oral Report = 30%
  • Grade-cutoffs
    • A = 93
    • A- = 90
    • B+ = 87
    • B = 83
    • B- = 80
    • C+ = 77
    • C = 73
    • C- = 70


There will be two exams given during the semester. These exams may be closed book and/or open book, in-class or in the testing center, as specified by the instructor prior to the exam. Exams will only be given after the scheduled date by special permission. Students with conflicts should arrange to take the exam prior to the scheduled date.


You will be required to complete a group project that is the final 3rd part of the course. One report will be submitted for the group.

Computer Tools

We will be using the open-source software package Python with matplotlib, mumpy, scipy, keras, scikit-learn and other machine learning packages. You need to do turn in your own version of the work, even if you work with other students.


I will come prepared to each class, ready to help explain the material and engage students in an active learning environment. I appreciate attentive students who respect my time and the time of other students.

Honor Code

President Henry B. Eyring has encouraged us to make this the type of university where Christ would like to come. He is also very interested in justifying the tithing money of faithful members of the church. It is such a pleasure to work at this university with such great young men and women. Please remember to adhere to the Honor Code and the Dress and Grooming Standards.

Study Habits

Grade Expectations

A Read or watch material in advance. Be attentive and ask questions in lectures, understand and do all homework on time, study hard for exams well before the exam starts, work hard and perform well on exams.

B Skim material in advance, attend lectures and try to stay awake, depend on TA for homework help, casually study for the exam by working the practice exam instead of learning concepts.

C Never prepare for class or work on other homework during class, skip some homework assignments, start studying for the exam the night before the exam.

D Skip class, don't turn in homework or turn it in late, start learning during the exam.

Preventing Sexual Misconduct

As required by Title IX of the Education Amendments of 1972, the university prohibits sex discrimination against any participant in its education programs or activities. Title IX also prohibits sexual harassment—including sexual violence—committed by or against students, university employees, and visitors to campus. As outlined in university policy, sexual harassment, dating violence, domestic violence, sexual assault, and stalking are considered forms of “Sexual Misconduct” prohibited by the university.

University policy requires any university employee in a teaching, managerial, or supervisory role to report incidents of Sexual Misconduct that come to their attention through various forms including face-to-face conversation, a written class assignment or paper, class discussion, email, text, or social media post. If you encounter Sexual Misconduct, please contact the Title IX Coordinator at or 801-422-2130 or Ethics Point at or 1-888-238-1062 (24-hours). Additional information about Title IX and resources available to you can be found at

Disability Resources

If you suspect or are aware that you have a disability, you are strongly encouraged to contact the University Accessibility Center (UAC) located at 2170 WSC (801-422-2767) as soon as possible. A disability is a physical or mental impairment that substantially limits one or more major life activities. Examples include vision or hearing impairments, physical disabilities, chronic illnesses, emotional disorders (e.g., depression, anxiety), learning disorders, and attention disorders (e.g., ADHD). When registering with the UAC, the disability will be evaluated and eligible students will receive assistance in obtaining reasonable University approved accommodations.