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

CSCE 4013-002 - Special Topics: Advanced Network Threat Analysis - Dr. Chris Farnell

In today's rapidly evolving digital landscape, cybersecurity is of paramount importance. This cybersecurity course explores packet-level network analysis, equipping students with the knowledge and skills to detect, analyze, and mitigate network threats effectively. Through hands-on labs and practical exercises, students will gain a comprehensive understanding of deep-packet inspection and threat analysis using common open-source tools. Prerequisite: CSCE 3193 Programming Paradigms or CSCE 3193H Honors Programming Paradigms. 

CSCE 4013-003 - Special Topics: Introduction to Machine Learning - Dr. Ukash Nakarmi

Introduction to Machine Learning (ML) covers several machine learning tools such as: logistic regression, ensemble methods, support vector machines, kernel methods, neural networks, Bayesian inference, reinforcement learning, learning theory, and their applications. This introductory course will focus on applications of these tools on several structured and unstructured data. Students will gain hands-on experience of developing machine learning algorithms using Python and sickit-learn. Prerequisite: CSCE 3193 Programming Paradigms or CSCE 3193H Honors Programming Paradigms. 

CSCE 4123 - Programming Challenges - Dr. Yarui Peng

CSCE 4273 - Big Data Analytics and Management - Dr. Lu Zhang

CSCE 4613 – Artificial Intelligence – Dr. Khoa Luu

CSCE 4813 - Computer Graphics - Dr. John Gauch

CSCE 4853 - Information Security - Dr. Brajendra Panda

 

Spring 2024 Graduate Electives

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

CSCE 5013-001 - Advanced Special Topics: Artificial Intelligence Ethics - Dr. Lu Zhang

Artificial intelligence and machine learning play a crucial role in vital decision-making processes across various sectors like industries, governments, and universities. However, the manifestation of algorithmic bias in these automated decisions has the potential to influence all facets of society. The challenge thus lies in developing algorithms that can efficiently process the extensive datasets they are trained on, while also maintaining fairness and equity in their outcomes. In this course, we will explore a range of AI/ML methodologies aimed at mitigating algorithmic bias and adressing the possible adverse effects of learning from big data, while also paying attention to the effects of these technologies on individuals, organizations, and society.

CSCE 5013-006 - Advanced Special Topics: Post Moore's Law Computer Architecture - 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 5073 - Data Mining - Dr. Xintao Wu

CSCE 5373 - Electronic Design Automation - Dr. Yarui Peng

CSCE 5653 - Network Security - Dr. Kevin Jin

CSCE 5683 - Image Processing - Dr. Khoa Luu

CSCE 5763 - Privacy Enhancing Technology - Dr. Qinghua Li

CSCE 5843 - Reconfigurable Computing - Dr. Miaoqing Huang

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-2/