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EECE 483: Machine Learning for Engineers

Electrical & Computer Engineer · 4 credits · Electrical&ComputerEngineering

Provides an introduction to machine learning with particular attention on real-world engineering applications. Theoretical foundation and application of supervised learning techniques such as regression and classification as well as unsupervised learning techniques such as clustering and dimensionality reduction. State-of-the-art deep learning algorithms as well as their implementation and use in solving engineering problems. Applications may include object detection and identification in images/videos, pattern recognition in speech/audio, and traffic prediction.

Offered in 1 of the 26 listed terms (Winter 2027).

Prerequisites

(EECE 244 or Electrical Engineerng OBSOLETE 244) and MATH 204 or (MATH 346 or EECE 346 or EECE 39YE)

Prerequisite tree

Each course's own prerequisites, down to the first courses in the chain.

From Banner's catalog for Spring 2027. Banner lists only the requisites it enforces, so check the catalog entry too.

Required by (0)

No course lists it as a prerequisite.

Syllabi and course websites

None listed yet.

Instructors

InstructorSectionsTerms
Khan, Junaid1Winter 2027

Every offering in the listed terms (1 section)

TermSecCRNTypeMeetsInstructorEnrolledDelivery
Winter 2027013444Lecture/LabMWF 10:00am-10:50am · KB 213
R 12:00pm-1:50pm · KB 211
Khan, Junaid0/30Face to Face Instruction