Courses
The following are examples of courses taught by LINCD faculty. For the most up-to-date information on courses that will be offered in upcoming semesters, please .Ìý
Undergraduate Courses
- Â Linear Systems
- Â Introduction to Probability
- Â Communication Theory
- Â Introduction to Digital Filters
- Â Communications Lab
- Data and Network Science
- ECEN 4672/5672 Digital Image Processing
- Â Deep Learning and Its Connections to Information Theory
Graduate Courses
- Fall (AÂ bold font implies the course will be offered every year. Otherwise, it is offered every other year)
- Â Noise and Random Processes
- Information Theory and Coding
- Introduction to Digital Filtering
- ECEN 4672/5672 Digital Image Processing
- Â Deep Learning and Its Connections to Information Theory
- Spring
- Modern Signal Processing
- Data and Network Science
- Â Principles of Digital Communication
- Â Machine Learning for Engineers
- Â Artificial Intelligence: Reasoning and Overview
- Communication Laboratory
- ECEN 5002/5772 Digital Video
Suggested Supplemental Courses for MS Students
- MS students are required to take at least four graduate courses on this page.
- Courses related to  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
- CSCI 5254 Convex Optimization and Its Applications
- or ​APPM 5630 Advanced Convex Optimization
- CSCI7000-013 Learning and Sequential Decision Making
- ECEN 5008 Online Convex Optimization
- APPM 5560 Markov Processes, Queues, and Monte Carlo Simulations, APPM 6550 Introduction to Stochastic Processes
- Discrete Mathematics and Number Theory
- Matrix Analysis
- APPM 5520Â Introduction to Mathematical Statistics I
- CSCI 5922 Neural Networks and Deep Learning
- Math 6310 Real Analysis I, Math 6320Â Â Â Real Analysis II
- Or APPM 5440 Applied Analysis 1, APPM 5450 Applied Analysis 2