Certificate: Engineering Applications of Artificial Intelligence

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OVERVIEW OF REQUIREMENTS

Minimum number of credits required to earn certificate: 12

Minimum Cumulative GPA required to earn certificate: 2.5

Minimum Grade requirements for courses to count toward certificate: A minimum grade of C in each course

Contact Information: Mohamad Musavi, Associate Dean, College of Engineering, 205 AMC Building, (207)581-2218, musavi@maine.edu; and Yifeng Zhu, Professor of Electrical and Computer Engineering, 271 Engineering Science Research Building, (207)581-2499, yifeng.zhu@maine.edu

Requirements: The proposed free-standing certificate program requires the completion of 12 credits (4 courses) through a combination of 400- and 500-level courses as described in the proposed course sequence section below. A maximum of one course (or 3 credits) at 400 or 500 level may be transferred from outside of UMaine to the program for credit.

Prerequisites: Undergraduate applicants should have taken at least one course in each of the following three areas:

college calculus (MAT 228 or equivalent),
statistics and probability (STS 132, STS 332, STS 434, ECE 316, CHE 350 or equivalent), and
engineering-level software programming (ECE 177, MEE 125, CIE 115, PHY 241, COS 125 or equivalent).

Class assignments in core courses listed below will use Python or Matlab. If students have extensive programming experiences in a different language (C/C++/Java/JavaScript/Perl/R), we
recommend they familiarize themselves with Python and Matlab before their first course. In addition, it is expected that students who take COS 470 should have COS 226 (data structures
and algorithms) or equivalent.

Educational Objectives:

Gain sufficient knowledge of AI methods for developing engineering applications based on modern AI tools.
Develop skills necessary for analyzing and processing data, and designing, implementing, and testing AI methods using available tools such as Matlab and Python.
Establish suitability and validation of AI methods for engineering applications.

The courses completed for this certificate program may be counted towards an undergraduate or graduate degree per the guidelines of the respective undergraduate or graduate degree program.

The program shall be completed within 3 years from the date of acceptance into the program.

Suggested Course Sequence:

The proposed certificate program requires completion of 12 credits of coursework (4 courses) divided into two categories of core and elective courses as described below. Elective courses are
classified into two categories: either (Category I) expose a new perspective of AI to increase the breadth, such as Fuzzy Logic, or (Category II) create an opportunity to apply AI to specific
applications. All electives involve a course project, such as robotics and medical image analysis. All students in this certificate program must apply appropriate AI approaches learned from core
courses to projects in elective courses.

Core Courses (6 credit hours)

COS 470 - Introduction to Artificial Intelligence Credits: 3
or
ECE 490 - Neural Networks Credits: 3
ECE 491 - Deep Learning Credits: 3

Elective Courses (6 credits hours, any two courses from the list below:)

Category I:
COS 470 - Introduction to Artificial Intelligence Credits: 3
COS 475 - Machine Learning Credits: 3
ECE 490 - Artifical Neural Networks Credits:3
ECE 577 - Fuzzy Logic Credits: 3
Category II:
BEN 451 - Biological and Medical Image Analysis I Credits: 3
BEN 452 - Biological and Medical Image Analysis II Credits: 3
ECE 417 - Mobile Robotics Credits: 3
ECE 533 - Advanced Robotics Credits: 3
MEE 444 - Robot Dynamics and Control Credits: 3
MEE 459 - Engineering Optimization Credits: 3
or
CIE 521 - Civil Engineering Systems and Optimization Credits: 3