MECHENG 5775.01
Transcript Abbreviation:
Applied ML for MAE
Course Description:
Classical and modern methods of machine learning with specific applications to mechanical and aerospace engineering, and robotics.
Course Levels:
Undergraduate (1000-5000 level)
Graduate
Designation:
Elective
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours
(N/A)
Select if Repeatable:
Off
Maximum Repeatable Credits
(N/A)
Total Completions Allowed
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Hybrid Class (25-74% campus; 25-74% online)
Distance-Enhanced (1-24% campus; 75-99% online)
Prerequisites and Co-requisites:
Prereq: Math 2177 or 2174, or 2415 and 2568; and Physics 1250 or 1250H or 1260 or 2300; and CSE 1221 or 1222 or Engr 1181 or 1281.01H or 1281.02H or 1221 or 1222; and AeroEng 3581 or MechEng 2850 or 5463; or Grad standing in Engineering.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for CSE 5523, ECE 4194.02, or 7868.
Course Goals / Objectives:
Provide an applied hands-on introduction to machine learning as relevant to MAE and robotics. Focus will be on using software for specific applications rather than ML theory.
Work in teams to develop a machine learning system to model and control a robot that explores and navigates its world
Learn to analyze different data sets from classical mechanical and aerospace engineering and understand how machine learning techniques can help complement classical engineering analysis and simulation methods
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Overview: Machine Learning Fundamentals | 3.0 | 0.0 | 0.0 | 0 |
| Uses of machine learning in robotics and MAE (including fields such as fluids, solids, controls, automotive, etc.): model building, model reduction, nonlinear control, system identification, inference, sensing, diagnostics, etc. | 3.0 | 0.0 | 0.0 | 0 |
| Data processing and data issues | 3.0 | 0.0 | 0.0 | 0 |
| Introduction to robotics software (Robot Operating System) and machine learning software | 1.5 | 0.0 | 0.0 | 0 |
| Robot platform overview: control of wheeled robot/legged robot | 1.5 | 0.0 | 0.0 | 0 |
| Model building, inference, and sensor fusion: simple regression problems | 6.0 | 0.0 | 0.0 | 0 |
| Discrete decisions based on data: classification and clustering | 3.0 | 0.0 | 0.0 | 0 |
| Learning from actions: reinforcement learning vs optimal control, exploration vs exploitation | 3.0 | 0.0 | 0.0 | 0 |
| Robot sensors: IMUs, joint encoders, cameras, LIDARs, other sensing modalities | 3.0 | 0.0 | 0.0 | 0 |
| Sensing and vision: nonlinear regression and neural networks | 6.0 | 0.0 | 0.0 | 0 |
| Nonlinear/hybrid control and nonlinear system models using neural networks | 3.0 | 0.0 | 0.0 | 0 |
| Robustness and fault tolerance | 3.0 | 0.0 | 0.0 | 0 |
| Total | 39 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework assignments | 20% |
| Project assignments | 35% |
| Midterm | 20% |
| Final Exam | 25% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| An Introduction to Machine Learning | Miroslav Kubat | |
| The Robotics Primer | Maja Mataric |
ABET-CAC Criterion 3 Outcomes
(N/A)
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Substantial contribution (3-6 hours) | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| 2 | Some contribution (1-2 hours) | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
| 3 | Some contribution (1-2 hours) | an ability to communicate effectively with a range of audiences - pre-2019 EAC SLO (g) |
| 4 | Substantial contribution (3-6 hours) | an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
| 5 | Substantial contribution (3-6 hours) | an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
| 6 | Substantial contribution (3-6 hours) | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
| 7 | Some contribution (1-2 hours) | an ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Embedded Literacies Info
(N/A)
Attachments
(N/A)
Additional Notes or Comments
(N/A)