Dynamic Optimization

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(:title Dynamic Optimization:) (:keywords machine learning, LSTM, deep learning, nonlinear control, dynamic estimation, parameter estimation, dynamic optimization, engineering optimization, MATLAB, Python, differential, algebraic, modeling language, university course:) (:description Dynamic Optimization Course for Engineers at Brigham Young University:)

Machine Learning and Dynamic Optimization is a graduate level course on the theory and applications of numerical solutions of time-varying systems with a focus on engineering design and real-time control applications. Concepts taught in this course include physics-based and empirical modeling, machine learning classification and regression, nonlinear programming, estimation, and advanced control methods such as model predictive control.

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Course Outcomes

• Students will demonstrate proficiency in theory and applications for optimization of dynamic systems with physics-based and machine learned models.
• Students will be able to create a digital twin of a physical process that computes in parallel to a real-time microcontroller.
• Students will be able to numerically solve ordinary and partial differential equations with coupled algebraic constraints.
• Students will be able to collect and analyze time-series data to build data-driven automation strategies.
• Students will be able to articulate classification and regression results with statistical measures of success.
• Students will be able to formulate and execute a project that utilizes course topics in machine learning and optimization methods for a novel application.
• Students will be able to solve optimization problems with nonlinear, mixed integer, multi-objective, and stochastic characteristics.

Related Topics

• Engineering-specific programming (Python, Matlab) with treatment of numerical methods.
• Machine Learning for Engineers: building mathematical models for classification and regression based on training data to make empirical predictions or decisions.
• Cybersecurity for Engineers: assessing and mitigating risks from computer-based adversarial attacks on engineered systems.
• Data Science: using scientific methods, processes, algorithms and systems to extract knowledge and insights from data.
• Data Visualization: creating graphical representations of data to extract insights.
• Internet of Things: building cyber-physical systems that connect microcontrollers, sensors, actuators, and other embedded devices. Includes mechatronics, embedded systems, distributed systems, and networking.
• High Performance Computing: programming high-performance computers (e.g., supercomputers, cloud computing) to tackle computationally-intensive engineering problems.

Introduction

• Introduction to Dynamic Optimization (pdf)

<iframe width="560" height="315" src="https://www.youtube.com/embed/WCTTY4baYLk?rel=0" frameborder="0" allowfullscreen></iframe> (:htmlend:)

Professor

 John D. Hedengren
 Office: 801-422-2590, 330L EB
 Cell: 801-477-7341
 Contact: john.hedengren [at] byu.edu

John Hedengren leads the BYU PRISM group with interests in combining data science, optimization, and automation with current projects in hybrid nuclear energy system design and unmanned aerial vehicle photogrammetry. He earned a doctoral degree at the University of Texas at Austin and worked 5 years with ExxonMobil Chemical prior to joining BYU in 2011.

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(:title IEEE CSS Committee on Control Education:) (:keywords process control, automation, education, learn, IEEE, technical committee:) (:description Meeting Minutes, Initiatives, and Members of the IEEE Technical Committee on Control Education:)

Attach:control_education.png Δ

Meeting Schedule

The IEEE CSS Technical Committees meets twice per year with the ACC and CDC conferences.

<a href='https://byu.zoom.us/j/91411714288'> <button class="button"><span>Join Meeting 3 PM EDT May 27</span></button>

Technical Committee Scope

• University education and continuing education issues in control
• Methodology for improving the theory, practice and accessibility of control systems education
• Control education laboratories, experiments, computer aided design, distance and virtual education technologies
• General awareness among pre-college students and teachers of the importance of systems and control technology and its cross-disciplinary nature

Technical Committee Tasks

• Promote control with its cross-boundary nature as a field that spans science, technology, engineering and mathematics (STEM).
• Provide students at all levels, including pre-college, undergraduate, graduate, and post-graduate, the opportunity to explore the world of control engineering
• Organize workshops and special sessions on education bringing academia and industry together to facilitate learning experiences to attract students to control engineering
• Communicate to the public at large the control field
• Engage all technical committees in control education issues and activities
• Organize semiannual meetings of the committee and use them as a platform to promote the control field

Technical Committee Members

• Chair: John Hedengren
• co-Chair (IFAC): Antonio Visioli
• Ahmad Al-Dabbagh
• Anthony Rossiter
• Atanas Serbezov
• Brian Douglas
• Daniel Rivera
• Helon Vicente Hultmann Ayala
• Jeffrey Kantor
• Steffi Knorn
• Steve Brunton

Request to join Technical Committee by filling out application form. Indicate initiatives to lead, advise, or otherwise contribute. With the recent change in leadership, some long-standing contributors to this committee may be inadvertently missed. We value your contributions and look forward to working together.

Initiatives

Curate Control Education Material

• Resourcium from Brian Douglas
• CACHE Control Resources from John Hedengren

Control Education Survey

• Control Survey? from IFAC Control Education Technical Committee

Dynamics and Control Online Courses

• Dynamics and Control Channels
  • Anthony Rossiter
  • Brian Douglas
  • John Hedengren
  • Steve Brunton

Partnerships

AACC Technical Committee on Control Education

• Chair: Daniel Abramovitch

IFAC Technical Committee on Control Education

• Chair: Antonio Visioli

Presentations

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Publications

• Rossiter, J.A., Serbezov, A., Visioli, A., Žáková, K., Huba, M., A survey of international views on a first course in systems and control for engineering undergraduates, IFAC Journal of Systems and Control, 13, 100092, 2020. Article
• Rossiter, J.A., Hedengren, J.D., Serbezov, A., Technical Committee on Control Education: A First Course in Systems and Control Engineering, IEEE Control Systems Magazine, Volume 41, Issue 1, pp. 20-23, 18 Jan 2021, doi:10.1109/MCS.2020.3033106 Article | Preprint Δ

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John Hedengren leads the BYU PRISM group with interests in combining data science, optimization, and automation with current projects in hybrid nuclear energy system design and unmanned aerial vehicle photogrammetry. He earned a doctoral degree at the University of Texas at Austin and worked 5 years with ExxonMobil Chemical prior to joining BYU in 2011.

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(:title Dynamic Optimization:)

(:title Machine Learning & Dynamic Optimization:)

(:title Machine Learning and Dynamic Optimization:) (:keywords machine learning, LSTM, deep learning, nonlinear control, dynamic estimation, parameter estimation, dynamic optimization, engineering optimization, MATLAB, Python, differential, algebraic, modeling language, university course:)

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John Hedengren leads the BYU PRISM group with interests in combining data science, optimization, and automation with current projects in hybrid nuclear energy system design and unmanned aerial vehicle photogrammetry. He earned a doctoral degree at the University of Texas at Austin and worked 5 years with ExxonMobil Chemical prior to joining BYU in 2011.

• Engineering-specific programming (Python, Matlab) with treatment of numerical methods.
• Machine Learning for Engineers: building mathematical models for classification and regression based on training data to make empirical predictions or decisions.
• Cybersecurity for Engineers: assessing and mitigating risks from computer-based adversarial attacks on engineered systems.
• Data Science: using scientific methods, processes, algorithms and systems to extract knowledge and insights from data.
• Data Visualization: creating graphical representations of data to extract insights.
• Internet of Things: building cyber-physical systems that connect microcontrollers, sensors, actuators, and other embedded devices. Includes mechatronics, embedded systems, distributed systems, and networking.
• High Performance Computing: programming high-performance computers (e.g., supercomputers, cloud computing) to tackle computationally-intensive engineering problems.

Related Topics

Engineering-specific programming (Python, Matlab) with treatment of numerical methods.

Machine Learning for Engineers: building mathematical models for classification and regression based on training data to make empirical predictions or decisions.

Cybersecurity for Engineers: assessing and mitigating risks from computer-based adversarial attacks on engineered systems.

Data Science: using scientific methods, processes, algorithms and systems to extract knowledge and insights from data.

Data Visualization: creating graphical representations of data to extract insights.

Internet of Things: building cyber-physical systems that connect microcontrollers, sensors, actuators, and other embedded devices. Includes mechatronics, embedded systems, distributed systems, and networking.

High Performance Computing: programming high-performance computers (e.g., supercomputers, cloud computing) to tackle computationally-intensive engineering problems.

Course Outcomes

Introduction

1. Course Outcomes

Course Outcomes

• Students will demonstrate proficiency in theory and applications for optimization of dynamic systems with physics-based and machine learned models.
• Students will be able to create a digital twin of a physical process that computes in parallel to a real-time microcontroller.
• Students will be able to numerically solve ordinary and partial differential equations with coupled algebraic constraints.
• Students will be able to collect and analyze time-series data to build data-driven automation strategies.
• Students will be able to articulate classification and regression results with statistical measures of success.
• Students will be able to formulate and execute a project that utilizes course topics in machine learning and optimization methods for a novel application.
• Students will be able to solve optimization problems with nonlinear, mixed integer, multi-objective, and stochastic characteristics.

Introduction

Machine Learning and Dynamic Optimization is a graduate level course on the theory and applications of numerical solutions of time-varying systems with a focus on engineering design and real-time control applications. Concepts taught in this course include physics-based and empirical modeling, machine learning classification and regression, nonlinear programming, estimation, and advanced control methods such as model predictive control.

The Machine Learning and Dynamic Optimization course is a Graduate level course for engineers on the theory and applications of numerical solutions of time-varying systems with a focus on engineering design and real-time control applications. Concepts taught in this course include physics-based and empirical modeling, machine learning classification and regression, nonlinear programming, estimation, and advanced control methods such as model predictive control.

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• Introduction to Dynamic Optimization (pdf)

(:html:) <iframe width="560" height="315" src="https://www.youtube.com/embed/WCTTY4baYLk?rel=0" frameborder="0" allowfullscreen></iframe>

To address this concern, I have prepared Python and MATLAB software tutorials that assume very little knowledge of programming. Additionally, there is a collection of IPython notebooks that are for beginners with TCLab Python programming. There are also many excellent resources on the internet that give tutorial introductions to programming. Those students who have no or little programming experience can review these step-by-step instructional videos to gain some of the required background.

A Temperature Control Lab is required for exercises in this course.

 Office: 801-422-2590, 330L EB

A Temperature Control Lab (PID+MPC) is required for several exercises in this course.

• ChE263: Computational Tools for Engineers for MATLAB / Python

• Nonlinear Modeling, Estimation and Predictive Control in APMonitor, Hedengren, J. D. and Asgharzadeh Shishavan, R., Powell, K.M., and Edgar, T.F., Computers and Chemical Engineering, Volume 70, pg. 133–148, 2014. Article, Preprint
• Beal, L.D.R., Hill, D., Martin, R.A., and Hedengren, J.D., GEKKO Optimization Suite, Processes, Volume 6, Number 8, 2018, doi: 10.3390/pr6080106. Article (Open Access)

• Optimization Methods for Engineering Design, Parkinson, A.R., Balling, R., and J.D. Hedengren, 2013. PDF

Dynamic Optimization for Engineers is a graduate level course on the theory and applications of numerical methods for solution of time-varying systems with a focus on engineering design and real-time control applications. Concepts taught in this course include mathematical modeling, data reconciliation, machine learning, nonlinear programming, estimation, and advanced control methods such as model predictive control.

Dynamic Optimization for Engineers is a graduate level course on the theory and applications of numerical methods for solution of time-varying systems with a focus on engineering design and real-time control applications. Concepts taught in this course include mathematical modeling, data reconciliation, reinforcement learning, nonlinear programming, estimation, and advanced control methods such as model predictive control.

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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.

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Assignments

Mid-Term Exam

25%

Final Exam

25%

Arduino Project

10%

Final Project

30%

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 and the class projects.

You will be required to complete a course project. I will provide suggestions or you can do something of your own interest or something that is integrated with a campus or off-campus research project.

A Temperature Control Lab (PID+MPC) is required for several exercises in this course. The lab kits are available for bulk order ($25/kit) or individual order ($35/kit).

Instructions are also available to build a simplified (SISO) lab kit with an Arduino and bread-board ($49 for recommended parts).

	<option value="Basic (PID) and Advanced Control (MPC) Kit">Basic (PID) and Advanced Control (MPC) Kit $35.00 USD</option>

A Temperature Control Lab (PID+MPC) is required for several exercises in this course. The lab kits are available for bulk order ($25/kit) or individual order ($35/kit). Instructions are also available to build a simplified (SISO) lab kit with an Arduino and bread-board ($49 for recommended parts).

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	<option value="Basic (PID) Lab Kit">Basic (PID) Lab Kit $29.00 USD</option>
	<option value="Basic (PID) and Advanced Control (MPC) Kit">Basic (PID) and Advanced Control (MPC) Kit $35.00 USD</option>

</select> </td></tr> <tr><td><input type="hidden" name="on1" value="Firmware (can change later)">Arduino Firmware (can change later)</td></tr><tr><td><select name="os1">

	<option value="MATLAB and Simulink">MATLAB and Simulink </option>
	<option value="Python">Python </option>

</select> </td></tr> <tr><td><input type="hidden" name="on2" value="Other notes (e.g. EU Plug)">Other notes (e.g. EU Plug)</td></tr><tr><td><input type="text" name="os2" maxlength="200"></td></tr> </table> <input type="hidden" name="currency_code" value="USD"> <input type="image" src="https://www.paypalobjects.com/en_US/i/btn/btn_buynowCC_LG.gif0" name="submit" alt="PayPal - The safer, easier way to pay online!"> <img alt="" border="0" src="https://www.paypalobjects.com/en_US/i/scr/pixel.gif1" height="1"> </form> (:htmlend:)

Course Requirements

To register for the course, fill out a Personal Information Sheet.

A Temperature Control Lab (PID+MPC) is required for several exercises in this course. The lab kits are available for bulk order ($25/kit) or individual order ($35/kit). Instructions are also available to build a simplified (SISO) lab kit with an Arduino and bread-board ($49 for recommended parts).

 Office: 801-422-2590, 350 CB

Arduino Project

5% (:cellnr:) Final Project (:cell:) 25%

• ChE436: Process Dynamics and Control in Python (2016-current)

• ChE436: Process Dynamics and Control in MATLAB (2011-2015)
• ChE436: Process Dynamics and Control in Python (2016-)

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 t9coordinator@byu.edu or 801-422-2130 or Ethics Point at https://titleix.byu.edu/report-concern or 1-888-238-1062 (24-hours). Additional information about Title IX and resources available to you can be found at titleix.byu.edu.

• Discussion Group: https://groups.google.com/forum/#!forum/apmonitor

Class Participation

Article Review

5%

Assignments

10%

Mid-Term Exam

20% (:cellnr:) Final Exam (:cell:) 25%

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Class Preparation and Participation

10% (:cell:) Assignments (:cell:) 10%

Assignments / Participation

Assignments

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• Introduction to Dynamic Optimization (pdf)

 M/W/F 1-3 pm

 ChE 593R: Dynamic Optimization

 M/W/F 1-3 pm, 413? CB

(:title Dynamic Optimization:)

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(:cell width=5% align='right':) (:cell width=80%:)

• ChE436: Process Dynamics and Control for review on dynamic modeling and control

• ChE263: Computational Tools for Engineers for review on MATLAB / Python
• ME575: Optimization Techniques in Engineering for review on optimization
• ChE436: Process Dynamics and Control for review on dynamic modeling and control

• Courses
  • Computational Tools for Engineers for review on MATLAB / Python
  • Optimization Techniques in Engineering for review on optimization
  • Process Dynamics and Control for review on dynamic modeling and control

• Optimization Methods for Engineering Design, Parkinson, A.R., Balling, R., and J.D. Hedengren, 2013. Available at: https://apmonitor.com/me575/index.php/Main/BookChapters

• Nonlinear Modeling, Estimation and Predictive Control in APMonitor, Hedengren, J. D. and Asgharzadeh Shishavan, R., Powell, K.M., and Edgar, T.F., Computers and Chemical Engineering, Volume 70, pg. 133–148, 2014. Available at: https://dx.doi.org/10.1016/j.compchemeng.2014.04.013

• Online Resources
  • Discussion Group: https://groups.google.com/forum/#!forum/apmonitor
  • YouTube Channel: https://www.youtube.com/user/APMonitorCom (subscribe)
• Articles
  • Nonlinear Modeling, Estimation and Predictive Control in APMonitor, Hedengren, J. D. and Asgharzadeh Shishavan, R., Powell, K.M., and Edgar, T.F., Computers and Chemical Engineering, Volume 70, pg. 133–148, 2014. Available
• Books
  • Optimization Methods for Engineering Design, Parkinson, A.R., Balling, R., and J.D. Hedengren. Available at: https://apmonitor.com/me575/index.php/Main/BookChapters

at: https://dx.doi.org/10.1016/j.compchemeng.2014.04.013

• Optimization of Chemical Processes, Edgar, T.F., Himmelblau, D.M., and L.S. Lasdon, McGraw Hill, 2001.

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(:cell width=80%:) John Hedengren worked 5 years with ExxonMobil Chemical on Optimization solutions for the petrochemical industry. He conducts research in optimization methods, modeling systems, and applications in Chemical Engineering. The PRISM group is actively working on oil and gas drilling automation, reservoir engineering, process optimization, unmanned aerial vehicles, and systems biology. (:tableend:)

 ChE 693R: Dynamic Optimization

Professor

John Hedengren worked 5 years with ExxonMobil Chemical on Optimization solutions for the petrochemical industry. He conducts research in optimization methods, modeling systems, and applications in Chemical Engineering. The PRISM group is actively working on oil and gas drilling automation, reservoir engineering, process optimization, unmanned aerial vehicles, and systems biology.

Using computer software as a technique for solving dynamic optimization problems is the focus of this course. All homework assignments will require the use of a computer.

To address this concern, I have prepared software tutorials that assume very little knowledge of programming. There are also many excellent resources on the internet that give tutorial introductions to programming. Those students who have no or little programming experience can review these step-by-step instructional videos to gain some of the required background.

This is a dynamic optimization course, not a programming course, but some familiarity with MATLAB, Python, or equivalent programming language is required to perform assignments, projects, and exams. Students who complete the course will gain experience in at least one programming language.

(:title Dynamic Optimization for Engineers:) (:keywords nonlinear control, dynamic estimation, parameter estimation, dynamic optimization, engineering optimization, MATLAB, Python, differential, algebraic, modeling language, university course:) (:description Dynamic Optimization Course for Engineers at Brigham Young University:)

Chemical Engineering 693R

 Dynamic Optimization
 M/W/F ? am, ?413? CB

Dynamic Optimization for Engineers is a graduate level course on the theory and applications of numerical methods for solution of time-varying systems with a focus on engineering design and real-time control applications. Concepts taught in this course include mathematical modeling, data reconciliation, nonlinear programming, estimation, and advanced control methods such as model predictive control.

Professor:

 John D. Hedengren
 Office: 801-422-2590, 350R CB
 Cell: 801-477-7341
 Contact: john.hedengren [at] byu.edu

John Hedengren worked 5 years with ExxonMobil Chemical on Optimization solutions for the petrochemical industry. He conducts research in optimization methods, modeling systems, and applications in Chemical Engineering. The PRISM group is actively working on oil and gas drilling automation, reservoir engineering, process optimization, unmanned aerial vehicles, and systems biology.

Course Introduction

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Teaching Assistant

Required Text

We will use a set of course notes and instructional videos that take the place of the book. Everyone will have access to these notes and videos through this web-site.

Resources

Reading is essential to success in this course. There are a number of resources that are available on the course web-site or through external sources. Most of the reading will come from journal articles or book chapters. Below is a list of some supplementary resources.

• Optimization Methods for Engineering Design, Parkinson, A.R., Balling, R., and J.D. Hedengren. Available at: https://apmonitor.com/me575/index.php/Main/BookChapters
• Nonlinear Modeling, Estimation and Predictive Control in APMonitor, Hedengren, J. D. and Asgharzadeh Shishavan, R., Powell, K.M., and Edgar, T.F., Computers and Chemical Engineering, Volume 70, pg. 133–148, 2014. Available at: https://dx.doi.org/10.1016/j.compchemeng.2014.04.013
• Optimization of Chemical Processes, Edgar, T.F., Himmelblau, D.M., and L.S. Lasdon, McGraw Hill, 2001.

Computers

Using computer software as a technique for solving dynamic optimization problems is the focus of this course. All homework assignments will require the use of a computer.

Recitation Sessions

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

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

Grading

(:table border=0 width=50%:) (:cell:) Homework (:cell:) 20% (:cellnr:) Mid-Term Exam (:cell:) 20% (:cellnr:) Final Exam (:cell:) 30% (:cellnr:) Final Project (:cell:) 30% (:tableend:)

Grade Expectations

A Read 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 and the class projects.

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 read book, work on other homework during class, skip some homework assignments, start cramming 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.

Exams

There will be a mid-term and the final exam. 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.

Projects

You will be required to complete a course projects. I will provide suggestions or you can do something of your own interest or something that is integrated with a campus or off-campus research project.

Computer Tools

One of the most common questions that I receive from students who would like to take this class is, "How much programming experience is required to succeed in the class?"

To address this concern, I have prepared software tutorials that assume very little knowledge of programming. There are also many excellent resources on the internet that give tutorial introductions to programming. Those students who have no or little programming experience can review these step-by-step instructional videos to gain some of the required background. We can also hold recitation sessions in a computer lab outside of normal class times if there is need.

This is an optimization course, not a programming course, but some familiarity with MATLAB, Python, C++, or equivalent programming language is required to perform assignments, projects, and exams. Students who complete the course will gain experience in at least one of these programming languages.

I will come prepared to each class, ready to help explain the material covered in the reading. I appreciate attentive students who respect my time and the time of other students.

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.