Graduate Certificate in Deep Learning

Information about the credit-bearing, degree-leading graduate certificate in Deep Learning offered through UTA's Computer Science and Engineering Department

Our credit-bearing, degree-leading Deep Learning certificate is intended to give those who successfully complete it:

  • An ability to understand fundamental concepts of deep learning, such as matrix computation, classification, regression, unsupervised learning, semi-supervised learning and supervised learning
  • In depth knowledge of Convolution Neural Networks, Recurrent Neural Networks, Long Short-term Memory, Batch Normalization, Dropout, Stochastic Gradient Descent, Attention Networks, and Transformer
  • An ability to apply this knowledge to subject areas, such as image processing, text mining, speech recognition, health informatics and bioinformatics, and social networks data

This certificate is managed by:

  • Dr. Junzhou Huang, administrator, Associate Professor
  • Dr. Vassilis Athitsos, co-administrator, Professor
  • Dr. Bahram Khalili, Associate Professor of Instruction and Graduate Advisor

All completed certificate coursework can be applied towards an MS or PhD degree.

Admission Requirements

CSE certificate students are required to have an undergraduate preparation equivalent to a baccalaureate degree in Computer Science or Computer Engineering or in a technical field relevant to the CSE curriculum. Students without a proper academic background, as determined by the graduate advisor at the time of the admission review, will be required to complete the CSE 5300 Foundation of Computing course and earn a passing grade in addition to the four required graduate courses.

Individuals interested in the certificate program can apply for admission using the UTA application for admission via ApplyTexas. Should a certificate student wish to continue on to an MS or PhD degree program in the CSE department, the certificate courses may be used toward that advanced degree. Note that for admission to the MS degree program, all UTA and CSE graduate degree admission requirements, including GRE and GPA, would need to be met.

Course Requirements

The course requirements for the Deep Learning certificate are:

  • CSE 5300 – Foundation of Computing (**Required only if insufficient CSE background)
  • CSE 5301 – Data Modeling
  • CSE 5360 – Artificial Intelligence I
  • CSE 5368 – Neural Networks
  • CSE 6363 – Machine Learning

A grade of C or better and an overall GPA of 3.0 or higher is required in all courses counted towards the completion of the certificate. The certificate program consists of 4-5 existing courses. Students enrolled in the certificate program will take courses with students studying for master’s and/or PhD programs in the CSE Department.

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