CptS-545-jana.doppa-2025-09-22-11-16-58
Below is a syllabus template that includes WSU's required syllabus elements. Please complete all items highlighted in yellow.
Title of Course [AI in the Real World]
Prefix and Number [CptS 545]
Semester and Year [Spring 2026]
Number of Credit Hours [3]
Prerequisites [CptS 570 Machine Learning]
Course Details
Day and Time: [Tue, Thu 10:35 to 11:50am]
Meeting Location: [MURR 229]
Instructor Contact Information
Instructor Name: [Jana Doppa]
Instructor Contact Information: [EME 133, jana.doppa@wsu.edu]
Instructor Office Hours: [click here for best practices] [Thu 3-4pm in EME 133]
TA Name: [N/A]
TA Contact Information: [office location, phone, email]: [N/A]
TA Office Hours: [click here for best practices] [N/A]
Course Description
[This course introduces advanced artificial intelligence tools (e.g., surrogate modeling, integrating physics with machine learning, uncertainty quantification, adaptive experimental design, offline black-box optimization, offline RL, and offline constrained RL) to solve real-world problems in science, engineering, and industrial domains. Students will learn both modern AI methods and how to deploy, evaluate, and govern AI systems in practice with an emphasis on case studies and hands-on projects.]
Course Materials
Books: [Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy. MIT Press, March 2022 (freely available online). Bayesian Optimization by Roman Garnett. Cambridge Press, 2023 (freely available online). Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville. MIT Press (freely available online). ]
Other Materials: [insert materials and cost]
Fees: [insert]
|
Course Learning Outcomes (students will be able to:) |
Activities Supporting the Learning Outcomes | Assessment of the Learning Outcomes |
|---|---|---|
| [Understand the fundamentals of advanced AI tools including surrogate modeling, integrating physics with machine learning, uncertainty quantification, adaptive experimental design, offline black-box optimization, offline RL, and offline constrained RL; and articulate their relative strengths, limitations, and areas of application.] | [Homework exercises and guided discussions will reinforce core theoretical concepts of these advanced tools and ensure students can articulate the assumptions and trade-offs of different approaches.] | [Evaluate understanding of theoretical foundations, derivations, and comparative analysis of approaches to solve a given problem setting. Grade: 30%] |
| [Design, implement, and evaluate AI solutions on real-world data, making appropriate modeling choices, selecting evaluation metrics, and addressing robustness and reliability.] | [Programming assignments will provide hands-on experience with building and analyzing AI solutions, focusing on practical issues of efficiency, scalability, and statistical performance.] | [Assess students’ ability to implement, train, and analyze AI solutions, with attention to computational and statistical trade-offs. Grade: 30%] |
| [Critically engage with advanced AI research, including reading, presenting, and assessing scholarly papers by identifying contributions, evaluating methodological soundness, recognizing limitations, and proposing future directions.] |
[A semester-long project will allow students to apply advanced AI techniques to a real-world problem of their choice, demonstrating creativity, critical evaluation, and interdisciplinary relevance.] |
[Assess the application of advanced AI methods to a real-world problem, including project design, execution, written report, and in-class presentation. Grade: 40%] |
| Dates | Lesson Topic | Assignment | Assessment |
|---|---|---|---|
|
Week 1 |
|
[Introduction] | [No] |
| Week 2 [dates] |
|||
| Week 3 [dates] |
|||
| Week 4 [dates] |
|||
| Week 5 [dates] |
|||
| Week 6 [dates] |
|||
| Week 7 [dates] |
|||
| Week 8 [dates] |
|||
| Week 9 [dates] |
|||
| Week 10 [dates] |
|||
| Week 11 [dates] |
|||
| Week 12 [dates] |
|||
| Week 13 [dates] |
|||
| Week 14 [dates] |
|||
| Week 15 [dates] |
Expectations for Student Effort
[Describe how much time students should expect to invest in the course each week. Graduate courses should state: "For each hour of lecture equivalent, students should expect to have a minimum of two hours of work outside of class." Note that Global campus courses will automatically include credit hour equivalents in the syllabus.] [Students are expected to attend all classes in person. Zoom recordings will only be made available to students who get permission from Access Center. Missing more than 20% of the class sessions will result in a fail. For each hour of lecture equivalent, students should expect to have a minimum of three hours of work outside class. ]
Grading [add more lines if necessary]
| Type of Assignment (tests, papers, etc) | Points | Percent of Overall Grade |
|---|---|---|
| [Homework] | [100] | [30] |
| [Exam] | [100] | [20] |
| [Project] | [100] | [50] |
| Grade | Percent | Grade | Percent |
|---|---|---|---|
| A |
[85] |
C | [55] |
| A- | [80] | C- | [50] |
| B+ | [75] | D+ | [45] |
| B | [70] | D | [40] |
| B- | [65] | F | <40 |
| C+ | [60] |
[Provide information about how grades will be rounded (eg, if 89% earns a B+ and 90% earns an A-, what grade is given to a student with an 89.5?]
Attendance and Make-Up Policy
[Provide details on how attendance affects final course grades. Indicate whether and how missed exams, laboratory sessions, etc. can be made up. Sample attendance statement: “Students should make all reasonable efforts to attend all class meetings. However, in the event a student is unable to attend a class, it is the responsibility of the student to inform the instructor as soon as possible, explain the reason for the absence (and provide documentation, if appropriate), and make up class work missed within a reasonable amount of time, if allowed. Missing class meetings may result in reducing the overall grade in the class.” ] [Students are expected to attend all classes in person. Zoom recordings will only be made available to students who get permission from Access Center. ]
Academic Integrity Statement
You are responsible for reading WSU's Academic Integrity Policy, which is based on Washington State law. If you cheat in your work in this class you will:
-[
-Receive a failing grade for the assignment or exam, and possibly a failing grade for the course.
-Be reported to the Office of Student Conduct, where further university-level sanctions (including probation, suspension, or dismissal) may be imposed.
]
-Be reported to the Center for Community Standards
-Have the right to appeal my decision
-Not be able to drop the course of withdraw from the course until the appeals process is finished
If you have any questions about what you can and cannot do in this course, ask me.
If you want to ask for a change in my decision about academic integrity, use the form at the Center for Community Standards website. You must submit this request within 21 calendar days of the decision.