Systems Engineering, MSE

Curriculum

Foundation Courses

Choose at least one course from each area of Data Science, Systems Modeling and System Design and Optimization

Data Science

1. CIS 5190

   Applied Machine Learning

   or: CIS 5200 Machine Learning
   1
2. ESE 5140

   Graph Neural Networks
3. ESE 5280

   Estimation and Detection Theory
4. ESE 5380

   Machine Learning for Time-Series Data
5. ESE 5390

   Hardware/Software Co-Design for Machine

   As published: “Hardware/Software Co-Design for Machine Learning”
6. ESE 5420

   Statistics

   As published: “Statistics for Data Science”
   1
7. ESE 5460

   Principles

   As published: “Principles of Deep Learning”
   1
8. ESE 6450

   Deep Generative Models
   1
9. ESE 6500

   Learning in Robotics

Systems Modeling

1. ESE 5000

   Linear Systems Theory
2. ESE 5030

   Simulation Modeling and Analysis
3. ESE 5070

   Introduction to Networks and Protocols
4. ESE 5310

   Digital Signal Processing
5. ESE 6650

   Datacenter Architecture
6. ENM 5310

   Data-driven Modeling and Probabilistic Scientific Computing

   As published: “Data-driven Modeling and Probabilistic Scientific Computing”

System Design and Optimization

1. ESE 5060

   Introduction to Optimization Theory
2. ESE 5050

   Feedback Control Design and Analysis
   1
3. ESE 5430

   Human Systems Engineering
4. ESE 6050

   Modern Convex Optimization
5. ESE 6190

   Model Predictive Control
   2

ESE Elective

Select 1 ESE Elective 2

Technical Electives

Select 2 Technical Electives:

Any 5000 or 6000 level course in EAS, ENM, ESE, CIS, CIT, IPD, MEAM, or MSE 3

Application Area

Choose ESE 9990 or any two graduate level courses from one approved Application Area 4