Computer Science

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.

This course introduces students to fundamental techniques for mobile autonomous robotics. In this course, a robot is a computer mounted on a chassis with controllable wheels. To allow the robot to perceive its surroundings, a camera and several distance sensors are attached to the computer. This course is oriented towards the practical aspects of mobile robotics and students in groups solve a set of assignments on the robots. Furthermore, the course introduces relevant robotics theory and methods including control, navigation, and localization of the robot as well as problem solving with robots including hardware/software trouble shooting. Some methods for analyzing sensor data are also covered. The course finishes with a larger assignment.

This course introduces students to the foundations of game creation and provides an overview of different aspects of game development. Students learn C# Programming (industry standard), starting with console application, then GUI games on various platform with graphics, dialog boxes, and user control. The course includes an overview of topics including game architecture, interface design, graphics for games, audio for games, prototyping and play testing. Students implement their creative gaming ideas by using the latest gaming tools. The course requires students to take prerequisites.

This is an intermediate-level course on data science with a focus on machine learning methods and algorithms using Python. First, the course introduces the big picture of machine learning using various examples, while teaching techniques of how to do data visualization for various types of data, a very important subfield of machine learning. The course also addresses decision tree learning; learning linear separators; logistic regression; generalization and overfitting; model selection and regularization; linear regression; ensemble learning; unsupervised learning; neural network models, and principal component analysis. 

The course also covers prediction and classification tasks using artificial neural networks and deep neural network models, and how to inrperet the results of accurate but black-box machine learning algorithms. A thorough treatment of deep learning is covered through an advanced course, Advanced Data Science. 

This introductory course on intelligent robots and systems is at the intersection of machine learning, artificial intelligence, computer vision and control theory. Students learn the fundamentals of developing systems which can sense, plan and act in the world based on various topics from the domains. Emphasis is on algorithm design, probabilistic reasoning, decision making under uncertainty and learning to improve behaviors using data. The course requires students to take prerequisites. 

This course introduces key concepts and applications for the design of intelligent computer systems, or Artificial Intelligence (AI). Topics covered include heuristic search, game playing, logic, machine learning, deep learning,
natural language processing, robotics and image processing. Through interactive lectures, discussions, and assignments, students apply basic AI concepts and principles to develop modeling and analytical skills for problem-solving. Students create working programs that solve problems, reason logically, and/or improve their own performance. The class covers the history, different careers, and social/cultural impacts of AI as well. Finally, it prepares students to further explore and apply AI in research and application domains.