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
  •  Linear Systems
  •  Introduction to Probability
  •  Communication Theory
  •  Introduction to Digital Filters
  •  Communications Lab
  • Data and Network Science (AI/ML)
  • ECEN 4672/5672 Digital Image Processing (AI/ML)
  •  Deep Learning and Its Connections to Information Theory (AI/ML)
Graduate Courses
  • Fall (A bold font implies the course will be offered every year. Otherwise, it is offered every other year)
    •  Noise and Random Processes
    • Information Theory and Coding
    • Introduction to Digital Filtering
    • ECEN 4672/5672 Digital Image Processing (AI/ML)
    •  Deep Learning and Its Connections to Information Theory (AI/ML)
  • Spring
    • Modern Signal Processing
    • Data and Network Science (AI/ML)
    •  Principles of Digital Communication
    •  Machine Learning for Engineers (AI/ML)
    •  Artificial Intelligence: Reasoning and Overview (AI/ML)
    • Communication Laboratory
    • ECEN 5002/5772 Digital Video (AI/ML)
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
  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