Course Description
This course introduces computational methods for the analysis and design of complex aerospace systems and system-of-systems. We will first explore the use of statistical and machine learning techniques for analyzing and predicting system performance. We will then integrate these techniques with design and optimization methods for exploring and understanding trade-offs between design alternatives. Topics may include regression and classification methods, design of experiments, surrogate-based design, metaheuristic optimization, and multi-objective optimization.
Pre-requisites: IE 300 or equivalent statistical analysis course (or permission of the instructor).
Course Credits: 3 credit hours for undergraduates, 4 credit hours for graduates. Students registered for 4 credit hours will have extra or slightly modified assignments and/or exams.
Learning Outcomes
- Apply a computational framework using statistical and optimization methods towards the design and analysis of complex systems.
- Develop a theoretical foundation of statistical learning, design, and optimization methods.
- Implement the introduced methods through programming assignments and projects.
- Communicate technical information clearly and concisely in written assignments and/or an oral presentation.
Course Logistics
Course Materials
Links to course materials are provided at the top of the website. Links to assignments will be posted on the Schedule page.
There is no textbook for this course. The lectures are designed to be self contained with supplementary material occassionaly covered in PDF notes. Additional references that you might find helpful are listed below.
Course Tools
Links to course tools are provided at the top of the website.
Campuswire
All announcements and discussions will be handled on Campuswire. We recommend you set up notifications to keep up with announcements.
You can join the course Campuswire using this link: https://campuswire.com/p/GBAA919B7
Any questions about concepts, assignments, or course material should be made public to avoid answering the same question multiple times. Feel free to post anonymously to your peers or anonymously to everyone (including instructors) as desired. Messages regarding personal issues (e.g., sickness, leave, individual grades) should be messaged privately to the instructor(s).
GitHub
All assignments will be distributed using GitHub Classroom. If you have not used GitHub, there is a short tutorial available here.
See the Assignments page for more details regarding assignment distribution and submissions.
Gradescope
All assignment submissions and grades will be handled on Gradescope.
You can join the course Gradescope using this entry code: 8K2ZGB
Python
All coding assignments will be done with Python. Python is open-source, widely used, and has a very active support community (e.g., stack overflow). You are expected to already be proficient in Python.
See the Resources page for resources related to coding (e.g., suggestions on setting up a programming environment for this course).
Assignments
Homeworks: There will be a series of 4-6 homework assignments. Homeworks will contain a mix of theoretical and coding problems.
You are encouraged to work together on homeworks, but each student should prepare and submit their own work. Homework that is viewed as insufficiently distinct to warrant an independent submission will not be given credit, and, depending on the situation, may be submitted as cheating via the FAIR system.
Projects: This course will involve two projects (one individual, one group) that aim to integrate methods implemented in various homework assignments together into larger tasks. You will identify problems of interest and propose those problems to the instructor. The instructor will work with each individual or team to properly scope the problem.
While the group project will be a team effort, individual grades will be assigned based on the submitted work and peer review of individual contributions (as needed).
Exams: Two closed-book exams will be used to evaluate your fundamental understanding of the course material.
Drop Pollicy: Your lowest homework grade will be dropped.
Late Policy: Late homework and project submissions will be accepted up to 72 hours after the deadline with the following deductions: -10 points (within 24 hours of the deadline), -15 points (within 48 hours of the deadline), -20 points (within 72 hours of the deadline).
Grading
Your final grade will calculated from homeworks (40%), projects (30%), and exams (30%). The following grading scale will be used:
| Grade | Point Range |
|---|---|
| A | [93, 100) |
| A- | [90, 93) |
| B+ | [87, 90) |
| B | [83, 87) |
| B- | [80, 83) |
| C+ | [77, 80) |
| C | [73, 77) |
| C- | [70, 73) |
| D+ | [67, 70) |
| D | [63, 67) |
| D- | [60, 63) |
| F | < 60 |
Respect and Growth in the Classroom
The effectiveness of our course is dependent upon each of us to create a safe and encouraging learning environment that allows for the open exchange of ideas while also ensuring equitable opportunities and respect for all of us. Everyone is expected to help establish and maintain an environment where students, staff, and faculty can contribute without fear of personal ridicule, or intolerant or offensive language. We ask everyone to be ready to learn and grow in your respect and understanding of others, in addition to your understanding of the course material.
Inclusivity
A feeling of belonging and inclusion is critical to the success and health of our community. The Aerospace Engineering department has a committee called Aero’s Space to Belong. They offer office hours, one-on-one discussion, and a reporting process. If you experience conflict that undermines your or someone else’s feelings of belonging, please consider using these resources: https://aerospace.illinois.edu/diversity/reporting.
Accomodations
Any student with special needs or circumstances requiring accommodation in this course (e.g., disability-related academic adjustments and/or auxiliary aids) is encouraged to contact the instructor and the Disability Resources and Educational Services (DRES) as soon as possible. To contact DRES, you may visit 1207 S. Oak St., Champaign, call 333-4603, e-mail disability@illinois.edu or go to the DRES website. We will ensure these special needs are met.
Additional References
- G. James, D. Witten, T. Hastie, R. Tibshirani, 2017, An Introduction to Statistical Learning, Springer (available online).
- T. Hastie, R. Tibshirani, and J. Friedman, 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer (available online).
- I. Goodfellow, Y. Bengio, A. Courville, 2016, Deep Learning, MIT Press (available online).
- M. Nielson, 2015, Neural Networks and Deep Learning, Determination Press (only available online).
- C. Bishop, 2011, Pattern Recognition and Machine Learning, Springer (available online).
- K. Murphy, 2012, Machine Learning: a Probabilistic Perspective, MIT Press.
- R. H. Myers, D. C. Montgomery, C. M. Anderson-Cook, 2016, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Wiley.
- K. Deb, 2001, Multi-Objective Optimization using Evolutionary Algorithms, Wiley.