Courses

The following are courses taught by LINCD faculty.

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