NOTE: Descriptions for electives can be found on the CSCE Undergraduate Courses page.

CSCE 4123 – Programming Challenges – Dr. Wing Ning Li

CSCE 4143 – Data Mining – Dr. Xintao Wu

CSCE 4273 - Big Data Analytics and Management - Dr. Justin Zhan - Introduction to the tools and techniques for distributed data computing and management, big data analytics, scalable machine learning, and real-time streaming data analysis.  Students cannot receive credit for both CSCE 4273 and CSCE 5273.  Prerequisites: CSCE 3193 - Programming Paradigms or DASC 2103 - Data Structures & Algorithms.

CSCE 4333/5223 – Introduction to Integrated Circuit Design – Dr. Zhong Chen.  Cross-listed with CSCE 5223.

CSCE 4553 – Information Retrieval – Dr. Susan Gauch

CSCE 4613 – Artificial Intelligence – Dr. Khoa Luu

CSCE 4623 – Mobile Programming – Dr. Alexander Nelson

CSCE 4783 – Cloud Computing and Security – Dr. Miaoqing Huang

 

CSCE 5013-003 - Advanced Special Topics: Post Moore's Law Comp. Arch. - Dr. David Andrews

The end of Dennard scaling and slowdown of Moore’s law has ushered in a new era called Post Moore’s Law Computing.  This course will look at the trends, applications, and emerging architectures that are defining this new era.  The course will look at how big data analytics and machine learning applications are driving innovation in large scale near memory architectures, domain specific accelerators, and neuromorphic computing.  The course will also survey new proposed technologies that are emerging to augment and replace CMOS as our computing base.  Course materials will be drawn from the literature.  Students will be required to make presentations during the semester and submit a final project.

CSCE 5013-005 - Advanced Special Topics: Deep Learning - Dr. Thi Hoang Ngan Le

The course aims at understanding the fundamental of deep learning and its application in computer vision, natural language understanding and game theory. The course starts with basic multi-layer perceptron and then moves towards other complicated models such as convolutional neural networks, recurrent neural networks, attention, and generative adversarial network models. The course will end with deep reinforcement learning. The course provides required steps for building deep learning models.  Prerequisite: CSCE Graduate Standing.  Note: Student should have previous exposure to linear algebra (matrix multiplication, inversion, and eigenvectors), vector spaces (principal component analysis, vector distance), programming (Java, or Python, or C++), linux, and probability.

Decriptions for the following electives can be found on the CSCE Graduate Courses page.

CSCE 5063 – Machine Learning – Dr. Xintao Wu

CSCE 4333/5223 – Introduction to Integrated Circuit Design – Dr. Zhong Chen.  Cross-listed with CSCE 4333.

CSCE 5323 – Computer Security – Dr. Dong "Kevin" Jin

CSCE 5333 – Computer Forensics – Dr. Brajendra Panda

CSCE 5533 – Advanced Information Retrieval – Dr. Susan Gauch

CSCE 5763 – Privacy Enhancing Technologies – Dr. Qinghua Li

Sponsored by the National Science Foundation and managed by the Arkansas Security Research and Education (ASCENT) Institute, the UofA Scholarship for Service (SFS) program is now accepting applications from eligible undergraduate and graduate students in CSCE, INEG, and ELEG at UofA with the goal as developing a superior cybersecurity workforce. This program provides generous scholarships ($25,000 per year for undergraduate students and $34,000 per year for graduate students, plus the full amount of tuition and other educational allowance per year). Each scholarship recipient will need to agree to work at a government agency post-graduation for a period equal to the duration of the scholarship. For more information and application submission, please visit https://ascent.uark.edu/sfs/ The application is now closed.