Spring 2027 Courses

How organizations run on software, and how to build software that organizations can run on. The core enterprise processes and the systems that execute them; systems of record and systems of engagement; business process modeling and the translation from process to data model to application; integration between systems, interface contracts and APIs; legacy replacement, data migration and cutover; build versus buy versus configure and the business case; data governance, privacy, security and control. Students build, integrate, evaluate and orally defend working business applications using AI-assisted development tools, and assess where artificial intelligence does and does not belong within a business process. Emphasis on specification, code review, testing, systems integration and professional accountability for AI-generated work. In-class design examinations, written reports and formal oral presentations. Open only to students in the Computer Science and Business Honors Program.

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In addition to a comprehensive overview of cybersecurity practices, this course delves deeply into the mathematics underpinning modern security techniques. Topics include cryptographic algorithms [which rely on complex mathematical principles such as number theory, algebra, and probability]. By understanding the mathematical foundations, students will gain insight into how encryption, secure hashing, and other security measures are designed to protect sensitive information. [This mathematical perspective is crucial for developing robust security solutions and understanding the vulnerabilities that can be exploited in various systems.]

Principles and techniques for optimizing the performance of software. Topics include performance evaluation, algorithmic optimizations, cache behaviors, impact of microarchitecture on software implementation, compiler optimizations, vectorization, and parallelization.

Introduction to hardware architectures and parallel computing systems that facilitate high speed machine learning. Graphics processing units (GPUs), hardware architecture of parallel computers, memory allocation and data parallelism, multidimensional kernel configuration, kernel-based parallel programming, principles and patterns of parallel algorithms, application of parallel computing to machine learning. Students may not obtain credit for more than one of this course, DSCI 421, ECE 303 and ECE 403.

Principles and techniques for optimizing the performance of software. Topics include performance evaluation, algorithmic optimizations, cache behaviors, impact of microarchitecture on software implementation, compiler optimizations, vectorization, and parallelization.

This course introduces the fundamental principles and methods that enable robots to learn from data, demonstrations, and interaction with their environment. Topics include learning robot dynamics, imitation learning, reinforcement learning, model-based learning, and the integration of learning with planning and control. Students will explore how learning-based methods can complement traditional robotics approaches to enable adaptation, improve performance, and address complex tasks that are difficult to model explicitly. The course combines theoretical foundations with practical implementations in simulation and robotic systems.Any restrictions/prerequisites: CSE 360 or CSE260 or CSE340 or CSE440

Introduction to hardware architectures and parallel computing systems that facilitate high speed machine learning. Graphics processing units (GPUs), hardware architecture of parallel computers, memory allocation and data parallelism, multidimensional kernel configuration, kernel-based parallel programming, principles and patterns of parallel algorithms, application of parallel computing to machine learning. Students may not obtain credit for more than one of this course, DSCI 421, ECE 303 and ECE 403.