Courses

Undergraduate Courses

• ECEN 3300 Linear Systems

• ECEN 3810 Introduction to Probability

• ECEN 4242 Communication Theory

• ECEN 4632/5632 Introduction to Digital Filters

• ECEN 4752/5752 Communications Lab

• ECEN 4322/5322 Data and Network Science

• ECEN 4002/5002 Deep Learning

Graduate Courses

• Fall (A bold font implies the course will be offered every year. Otherwise, it is offered every other year)
  • ECEN 5612 Noise and Random Processes
  • ECEN 4002/5002 Deep Learning
  • ECEN 5622 Information Theory and Coding
  • ECEN 4632/5632 Introduction to Digital Filtering
• Spring
  • ECEN 5712 Machine Learning for Engineers
  • ECEN 5012 Modern Signal Processing
  • ECEN 5692 Principles of Digital Communication
  • ECEN 5022 Artificial Intelligence: Foundations and Overview
  • ECEN 4322/5322 Data and Network Science
  • ECEN 4752/5752 Communication Laboratory
  • ECEN 5682 Theory and Practice of Error Control Codes
• Tentative teaching schedule
• LINCD area introduction

Suggested Supplemental Courses for MS Students

• MS students are required to take at least four graduate courses on this page.
• Courses related to Embedded Systems is a great supplement.

Suggested Supplemental Courses for PhD Students

• Optimization, Linear Programming, Matrix Analysis, Courses from Applied Math and Computer Science Departments
• Real Analysis and Probability Theory from Math or Applied Math Department

1. CSCI 5254 Convex Optimization and Its Applications

   • or ​APPM 5630 Advanced Convex Optimization

2. CSCI7000-013 Learning and Sequential Decision Making

3. ECEN 5008 Online Convex Optimization

4. APPM 5560 Markov Processes, Queues, and Monte Carlo Simulations, APPM 6550 Introduction to Stochastic Processes

5. Discrete Mathematics and Number Theory

6. Matrix Analysis

7. APPM 5520  Introduction to Mathematical Statistics I 

8. CSCI 5922 Neural Networks and Deep Learning

9. Math 6310 Real Analysis I, Math 6320    Real Analysis II

   • Or APPM 5440 Applied Analysis 1, APPM 5450 Applied Analysis 2