Courses

The following are courses taught by LINCD faculty.

Undergraduate Courses
  • ECEN 3300 Linear Systems
    • Analysis and design of linear systems
  • ECEN 3810 Introduction to Probability
    • Probability theory with engineering examples
  • ECEN 4242 Communication Theory
    • Techniques for reliable communication via, e.g., cell phones
  • ECEN 4632/5632 Introduction to Digital Filters
    • Sampling and processing of digital signals
  • ECEN 4752/5752 Communications Lab
    • Experiment with software defined radios
  • ECEN 4322/5322 Data and Network Science (AI/ML)
    • Data manipulation and visualization techniques and basic machine learning
  • ECEN 4672/5672 Digital Image Processing (AI/ML)
    • ML techniques for image manipulation for brain perception
  • ECEN 4732/5732 Deep Learning and Its Connections to Information Theory (AI/ML)
    • Deep learning from scratch and its relation to information theory
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
      • Foundation for dealing with randomness in data and physical systems
    • ECEN 5622 Information Theory and Coding
      • Foundation for data compression, communication, and machine learning
    • ECEN 4632/5632 Introduction to Digital Filtering
      • Sampling and processing of digital signals
    • ECEN 4672/5672 Digital Image Processing (AI/ML)
      • ML techniques for image manipulation for brain perception
    • ECEN 4732/5732 Deep Learning and Its Connections to Information Theory (AI/ML)
      • Deep learning from scratch and its relation to information theory
  • Spring
    • ECEN 5592 Modern Signal Processing
      • Modern techniques for signal processing by taking advantage of their structures
    • ECEN 4322/5322 Data and Network Science (AI/ML)
      • Data manipulation and visualization techniques and basic machine learning
    • ECEN 5682 Theory and Practice of Error Control Codes
      • Protection of signals from corruption
    • ECEN 5692 Principles of Digital Communication
      • Techniques behind reliable communication via, e.g., cell phones
    • ECEN 5712 Machine Learning for Engineers (AI/ML)
      • Machine learning theory and algorithms
    • ECEN 5722 Artificial Intelligence: Reasoning and Overview (AI/ML)
      • Latest advancement in AI reasoning
    • ECEN 4752/5752 Communication Laboratory
      • Experiment with software defined radios
    • ECEN 5772 Digital Video (AI/ML)
      • ML techniques for video manipulation and the extraction of information from them
  • Tentative teaching schedule
  • Introduction to the area
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