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Machine Learning Certificate of Graduate Study

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Program Information

The Rowan Experience
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Machine Learning (ML) / Artificial Intelligence (AI) has been one of the fastest-growing fields in all sciences and engineering. Not a day goes by that we hear another story that talks about how machine learning and artificial intelligence have become part of our lives. The number of applications where machine learning systems make automated decisions in place of humans has grown exponentially. There is a significant demand in this area for qualified professionals, and these professionals have some of the highest starting salaries of any profession. Students graduating with a graduate degree in a machine learning related field typically receive salary offers that can reach $150,000 or higher.

The Certificate of Graduate Study (COGS) in Machine Learning will be offered by the Henry M. Rowan College of Engineering, Department of Electrical and Computer Engineering and will consist of four (4) graduate courses one of which can be project or thesis-based, as described below.

A unique aspect of this COGS is the practical hands-on experience that it provides: the COGS in Machine Learning features four classes (two required and two electives), each with its own major project component. One of the two electives may be Master’s or Doctoral level research, where students work on real-world unsolved problems. The students will need to focus their project / thesis-based course on machine learning.

Hence through this four-class sequence, the Machine Learning COGS will provide breadth and depth in theoretical foundations of machine learning, as well as practical hands-on, real-world project experience. As a result of this broad fundamental and practical experience, we expect our students to be particularly marketable, attracting high-paying salaries in a wide range of industries, including engineering, data science, biotechnology, business - both in private and public sectors.

Curriculum
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The Certificate of Graduate Study (COGS) in Machine Learning is a part-time program offered in a face-to-face format. It requires the completion of 12 graduate semester hours (4 courses). The following courses make up the Machine Learning Certificate of Graduate Study program.

  • 4 Courses / 12 Semester Hours
  • Foundation Courses: No
  • Graduation / Exit / Thesis Requirements: No
Course Number Title S.H. (Credits)
Required Courses: 6 S.H.
ECE 09555 Machine Learning 3
ECE 09558 Reinforcement Learning OR 3
ECE 09595 Advanced Emerging Topics in Computational Intelligence, Machine Learning and Data Mining  3
Elective Courses: 6 S.H.
ECE 09560 Modern Neural Networks 3
ECE 09566 Systems, Devices and Algorithms in Bioinformatics 3
ECE 09504 Special Topics in ECE (must be a section specific to Machine Learning) 3
ECE 09655 Advanced Computational Intelligence and Machine Learning 3
ENGR 01599 Master's Thesis Research (must be related to Machine Learning) 3
ENGR 01799 Doctoral Research (must be related to Machine Learning) 3
ECE 09558 Reinforcement Learning (if not taken as one of the required courses) 3
ECE 09595 Advanced Emerging Topics in Computational Intelligence, Machine Learning and Data Mining (if not taken as one of the required courses)  3
ME 10561 Engineering Optimization 3

Note: The courses listed above are not official and are subject to change. For an official list of available courses please visit the Rowan Global section tally.

Transfer Credit Evaluation Policy

This program may accept one course - if equivalent to one of the required or elective courses - as a transfer course. 

Admission Requirements
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The following is a list of items required to begin the application process for the program. There may be additional actions or materials required for admission to the program. Upon receipt of the materials below, a representative from the Rowan Global Admissions Processing Office will contact you with confirmation or will indicate any missing items.

  • Completed Application Form
  • $65 (U.S.) non-refundable application fee
  • Bachelor’s degree (or its equivalent) from an accredited institution of higher learning
  • Official transcripts from all colleges attended (regardless of number of credits earned)
  • Minimum undergraduate cumulative GPA of 3.0 (on a 4.0 scale)
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