Machine Learning Plan of Work

Suggested courses for Machine Learning

Machine learning aims to study algorithms that can learn from data in order to identify patterns and make predictions. Applications include medical diagnosis, autonomous navigation, computational advertising, visual object recognition, and many more. A student with a concentration on machine learning will learn the mathematical foundations that will allow him/her to learn standard techniques in machine learning as well as the state of the art new areas such as deep learning. Students will gain hand on experience through projects built-in within their coursework.

Suggested 500-level courses

  • ECE 514 – Random Process
  • ECE 542 – Neural Networks
  • ECE 558 – Digital Imaging Systems
  • ECE 592 – Topics in Data Science

Suggested 700-level courses

  • ECE 759 – Pattern Recognition and Machine Learning
  • ECE 763 – Computer Vision
  • ECE 765 – Probabilistic Graphical Models for Signal Processing and Computer Vision

Suggested courses for breadth

  • ECE 516 – System Control Engineering
  • ECE 547 – Cloud Computing Technology
  • ECE 556 – Mechatronics
  • ECE 560 – Embedded Systems Architecture
  • ECE 570 – Computer Networks
  • ECE 579 – Introduction to Computer Performance Modeling
  • ECE 726 – Advance Feedback Control
  • ECE 751 – Detection and Estimation Theory
  • ECE 752 – Information Theory

Suggested non-ECE courses

  • CSC 520 Artificial Intelligence
  • CSC 522 Automated Learning and Data Analysis
  • MA 505 Linear Programming
  • MA 518 Geometry of Curves and Surfaces
  • MA 523 Linear Transformations and Matrix Theory
  • MA 565 Graph Theory
  • MA 706 Nonlinear Programming
  • MA 797 Convex Analysis
  • ST 730 Time Series Analysis

Please see ECE Course Details and Specialty Areas for ECE specialty area and typical semesters that the courses are offered.

Please see NCSU Course Catalog for course description and the current semesters that the course is offered.

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