Computer Science

This course covers basic data representations, algorithms, and applications for interactive visualization. The class mainly focuses on computer graphics and spatial data visualization. 

Topics include Graphics systems, OpenGL basics, Transformations, Data acquisition, Data representation, Viewing, Lighting and shading, Shaders, Color models, Textures, Volume rendering, Surface visualization, Geometric processing, Image visualization, Advanced topics in visualization. 

This course covers concurrent activities, busy-wait and polling, synchronization and communication, atomic operations such as test-and-set, and mutual exclusion. Central aspects of the Java concurrent package, such as locks, semaphores, thread pools, tasks, and blocking queues are also reviewed. The course concludes with an overview of multicore hardware, real-time operating systems, and scheduling.  Entry requirements include Programming and a second course in Java.

This course covers the fundamental concepts of databases—an essential component in implementing e-business information systems—including the entity-relationship model, relational databases, and the use of structured query language (SQL). Through individual projects, students also explore how to integrate databases with business information systems. Topics include Introduction to Database Industrial Information Management, Introduction to Structured Query Language (SQL), Relational model and normalization, Database design using normalization, Data modelling with the entity-relationship model, Transforming data models into a database design, SQL for database construction and application processing, Database redesign, Managing multi-user databases, Web Server Environment, and Data warehouses, business intelligent systems, and big data. 

This course introduces computer vision with a focus on modern deep learning. We start with the foundational concepts and history of the field. We then dive into the key architectures that have shaped modern computer vision. We study convolutional neural networks (CNNs) and Vision Transformers (ViT), learning how they work and how they are used for fundamental tasks like image classification, object detection, and semantic segmentation. Then, we cover 3D computer vision, including problems like 3D reconstruction. Finally, students focus on deep generative models for vision, exploring how they are used to create realistic images and videos.  

Prior to taking this course, it is recommended that students take courses in linear algebra and probability and statistics.  

Topics include Introduction to Computer Vision; Basics of Digital Images and Processing; Machine learning and neural networks; Convolutional neural networks (CNNs); Computer vision problems; Vision transformers (ViTs) for computer vision; 3D Computer Vision; Generative Models: VAEs, GANs; and Generative Models: Diffusion Models, Multimodal models. 

This course examines how the Internet works and how everyday online activities generate data that are collected, analyzed, and monetized by digital platforms. It explores key issues related to data privacy, security, ownership, and control, addressing questions about how personal information is used and how individuals can protect themselves online. The course provides practical knowledge and tools for understanding Internet infrastructure, data tracking practices, and strategies for managing one’s digital presence with greater confidence and awareness.

The course covers advanced topics and techniques in big data, with a focus on the algorithmic and system aspects. It provides both theoretical and hands-on experience in big data and data mining. Topics include MapReduce, textual data management, graph data management, uncertain data management, association rule mining, and state-of-the-art data mining techniques. It also covers recent developments and progress in selected areas.

This course covers human-computer interaction (HCI) design methods and principles. Human-computer interaction deals with the design of interactive systems to support the ways people communicate and interact in their everyday and working lives. The central goal of HCI is to develop usable systems that are easy to learn, effective to use, and offer an enjoyable experience. 

In this course, students explore well-known design principles on usability aspects (e.g., learnability, efficiency, human errors) and design methodologies (e.g., user-centered design, task analysis, prototyping, heuristic evaluation, and user testing). Design assignments and term projects help students enhance their user interface design skills in web, mobile, and IoT environments.  

This course covers the nature of digital logic and numbering systems. Topics include: Basic gates, Boolean algebra, Karnaugh maps, memory elements, latches, flip-flops, design of combinational and sequential circuits, integrated circuits and logic families, shift registers, counters, multiplexers, demultiplexers, decoders, encoders, and parity circuits, Number systems, 1’s and 2’s complements, arithmetic circuits, fixed-point and floating-point representations, memory types, design of circuits using ROMs and PLAs. The course involves exposure to logic design automation software and an introduction to FPGAs and HDL. Prerequisite: fundamentals of computing. 

This is the laboratory component and corequisite of the DIGITAL LOGIC DESIGN (host institution course number ECNG 2101) course. It covers experiments in digital design and experiments illustrating material of the main course including an FPGA-based project.

This course covers the following topics: graph algorithms such as max flow; data structures such as van Emde Boas Trees; NP-completeness; exponential and parameterized algorithms for NP-hard problems; approximation algorithms; randomized algorithms; computational geometry; linear programming and optimization.