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 .
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Linear Systems
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Introduction to Probability
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Communication Theory
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Introduction to Digital Filters
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Communications Lab
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Data and Network Science
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ECEN 4002/5002 Deep Learning
- Fall (A bold font implies the course will be offered every year. Otherwise, it is offered every other year)
- Noise and Random Processes
- ECEN 4002/5002 Deep Learning
- Information Theory and Coding
- Introduction to Digital Filtering
- Introduction to Digital Filtering
- Special Topic: Deep Learning and Its Connections to Information Theory
- Spring
- Machine Learning for Engineers
- Modern Signal Processing
- Principles of Digital Communication
- Special Topic: Artificial Intelligence: Foundations and Overview
- Data and Network Science
- Communication Laboratory
- Theory and Practice of Error Control Codes
- MS students are required to take at least four graduate courses on this page.
- Courses related to is a great supplement.
- Optimization, Linear Programming, Matrix Analysis, Courses from Applied Math and Computer Science Departments
- Real Analysis and Probability Theory from Math or Applied Math Department
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CSCI 5254 Convex Optimization and Its Applications
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or 鈥婣PPM 5630 Advanced Convex Optimization
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CSCI7000-013 Learning and Sequential Decision Making
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ECEN 5008 Online Convex Optimization
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APPM 5560 Markov Processes, Queues, and Monte Carlo Simulations, APPM 6550 Introduction to Stochastic Processes
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Discrete Mathematics and Number Theory
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Matrix Analysis
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APPM 5520 Introduction to Mathematical Statistics I
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CSCI 5922 Neural Networks and Deep Learning
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Math 6310 Real Analysis I, Math 6320 Real Analysis II
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Or APPM 5440 Applied Analysis 1, APPM 5450 Applied Analysis 2
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