Intelligent Systems in Engineering Applications

This certificate program introduces students to the core concepts of intelligent systems and a broad range of techniques for building, testing and evaluating intelligent systems. Topics include: intelligent system design, training and evaluation, decision trees, rule based systems, Bayesian learning, Support Vector Machines, neural network systems, and fuzzy systems. A variety of application cases will be studied in the courses under this program. (12 credits hours)

Certificate offered on Campus and via Distance Learning 

Required Core Courses

ECE 579Intelligent Systems3
Additional Coursework
Complete 3 courses from the following (9 credits):
ECE 537Data Mining3
ECE 552Fuzzy Systems3
ECE 576Information Engineering3
ECE 583Artificial Neural Networks3
ECE 585Pattern Recognition3
 

ECE 537     Data Mining     3 Credit Hours

Introduction to the fundamental concepts of data mining including data exploration, pre-and post-processing, OLAP, predictive modeling, association analysis, and clustering. This course also focuses on the analysis of algorithms commonly used for of data mining applications, mining structured, semi-structured and unstructured data, stream data, and web data. Team oriented course project to provide hands-on experience may be required. Three lecture hours per week.

Restriction(s):
Can enroll if Class is Specialist or Graduate or Doctorate

ECE 552     Fuzzy Systems     3 Credit Hours

A study of the concept of fuzzy set theory including operations on fuzzy sets, fuzzy relations, fuzzy measures, fuzzy logic, with an emphasis on engineering application. Topics include fuzzy set theory, applications to image processing, pattern recognition, artificial intelligence, computer hardware design, and control systems.

Restriction(s):
Can enroll if Class is Graduate

ECE 576     Information Engineering     3 Credit Hours

This course will cover fundamental concepts of information engineering, including theoretical concepts of how information is measured and transmitted, how information is structured and stored, how information can be compressed and decompressed, and information networks such as social networks, affiliation networks and online networks, mathematical theories of information networks. Information engineering applications will be discussed. Three lecture hours per week.

Restriction(s):
Can enroll if Class is Graduate
Can enroll if Level is Doctorate or Rackham or Graduate
Can enroll if Major is Computer Engineering, Software Engineering, Electrical Engineering, Computer & Information Science

ECE 579     Intelligent Systems     3 Credit Hours

Representative topics include: Intelligent systems design, training and evaluation, decision trees, Bayesian learning, reinforcement learning. A project will be required.

Restriction(s):
Can enroll if Level is Rackham or Graduate or Doctorate
Can enroll if Major is Computer & Information Science, Software Engineering, Electrical Engineering, Computer Engineering

ECE 583     Artificial Neural Networks     3 Credit Hours

Students will gain an understanding of the language, formalism, and methods of artificial neural networks. The student will learn how to mathematically pose the machine learning problems of function approximation/supervised learning, associative memory and self-organization, and analytically derive some well-known learning rules, including backprop. The course will cover computer simulations of various neural network models and simulations. Three lecture hours per week.

Restriction(s):
Can enroll if Class is Graduate
Can enroll if Level is Graduate or Rackham or Doctorate
Can enroll if Major is Computer Engineering, Electrical Engineering, Computer & Information Science, Software Engineering

ECE 585     Pattern Recognition     3 Credit Hours

Introduction to pattern recognition (PR) as a process of data analysis. Representation of features in multidimensional space as random vectors. Similarity and dissimilarity measures in feature space. Bayesian decision theory, discriminant functions and supervised learning. Clustering analysis and unsupervised learning. Estimation and learning. Feature extraction and selection. Introduction to interactive techniques in PR. Applications of PR.

Prerequisite(s): IMSE 317

Restriction(s):
Can enroll if Major is Computer Engineering, Electrical Engineering, Computer & Information Science

 
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An asterisk denotes that a course may be taken concurrently.

Frequency of Offering

The following abbreviations are used to denote the frequency of offering: (F) fall term; (W) winter term; (S) summer term; (F, W) fall and winter terms; (YR) once a year; (AY) alternating years; (OC) offered occasionally