Courses | VD Academic Affairs

This course aims to introduce students to theoretical models of computation while providing an in-depth understanding of the subject. It offers a comprehensive exploration of the hierarchies of formal languages and non-deterministic automata, highlighting their interconnections. Key concepts such as computability and decidability are also introduced. Additional topics include: the role of formal languages and automata in the study of computability and complexity, finite state automata and regular expressions, pushdown automata and context-free grammars, as well as Turing machines and computable functions, decidable and undecidable languages.

History and overview of distributed systems. Fundamentals of distributed systems and algorithms. Problems, methodologies, paradigms and models necessary for understanding and designing distributed applications. Topics may include: system models, process communication, distributed shared memory, web services, fault tolerance, real/logical/virtual time, distributed algorithms, authentication and security, naming, object-oriented structuring of distributed systems, distributed file systems, communication support for distributed systems. Theoretical concepts will be complemented with practical examples.

The course will offer a concise introduction to quantum computation, developing the basic elements of this new branch of computational theory without assuming any background in physics. It begins with a novel introduction to the quantum theory from a computer-science perspective. It illustrates the quantum-computational approach with several elementary examples of quantum speed-up, before moving to the major applications: Shor's factoring algorithm, Grover's search algorithm, and quantum error correction.

This course will introduce data mining techniques, including frequent pattern and association rule mining, some basic background on classification and clustering, and applications of data mining techniques in specific domains. The emphasis will be on applications in specific domains rather than fundamental methodologies. The course will begin with an introduction to the data mining field, covering essential topics such as the significance of data mining, its definition, the types of data that can be mined, and the various patterns that can be extracted., Additionally, it will provide an overview of relevant technologies, discuss major challenges in data mining, and offer a brief history of the data mining community.

This is an undergraduate-level introductory course for information retrieval. It will cover algorithms, design, and implementation of modern information retrieval systems. Topics include: retrieval system design and implementation, text analysis techniques, retrieval models (e.g., Boolean, vector space, probabilistic, and learning-based methods), search evaluation, retrieval feedback, search log mining, and applications in web information management.

This course introduces fundamental component and algorithms of natural language processing (NLP) through computational methods with a focus on machine learning techniques. Topics include NLP tasks and semantics, N-gram language models, which form the basis for understanding linguistic patterns and structures, part-of-speech tagging and sequence labeling, and synaptic parsing and semantic analysis. Design and implantation of NLP models using neural networks and transforms. Practical applications will include information extraction, chatbots and dialogue systems, text-to-speech, and machine translation.

This course is concerned with the computer acquisition and analysis of image data. Computer vision is the construction of explicit meaningful descriptions of physical objects or other observable phenomena from images. The emphasis is on physical, mathematical, and information-processing aspects of the vision. Topics to be covered include image formation, edge detection and segmentation, convolution, image enhancement techniques, extraction of features such as color, texture, and shape, object detection, 3-D vision, and computer vision system architectures and applications.

The nature of robotics and the role of intelligence in the context of robotics. Overview of robotic systems: state-of-the-art robot systems, planning vs. reactive control, uncertainty in control, sensing, and world models. Configuration space. The role of planning in robotics and relevant techniques. Robot programming, the range of software that supports robotic activity. Navigation and control, strategies for particular environments. Ethical issues associated with robotics and intelligent behavior.

Topics include the development of decision support and expert systems, knowledge representation and processing, and the design of knowledge base to transfer human knowledge into expert systems. The course will also explore tools for building expert systems logic programming, expert system shells (Clips, Jess, VISIRULE etc.). Emphasis will be placed on leveraging domain-specific knowledge to obtain expert performance in programs.

This course is designed for senior in Computer Engineering major to introduce them to the fundamentals and theories of machine learning algorithms. Students will be taught the theory, design and implementation of different machine learning algorithms such as Bayes classifiers, decision trees, neural networks, DNN, evolutionary algorithms, inductive learning. Students will implement and compare different algorithms for learning problems. The project is an integral component of this course.

This course explores the cutting-edge techniques in interactive computer graphics, photorealistic rendering, VR/AR, and GPU programming. Covers advanced 3D transformations, shading models, global illumination, real-time ray tracing, physically based rendering, neural rendering, and immersive graphics for virtual and augmented reality. Hands-on projects using modern graphics APIs and engines.

This course introduces fundamental components of deep learning system, including Linear Classifiers, Stochastic Gradient Descent, Fully Connected Networks, Convolutional Networks, and Recurrent Networks. Additionally, it introduces applications of deep learning, emphasizing the core concepts behind these applications, such as Object Detection, Image Segmentation, Video Classification, Generative Models, and Reinforcement Learning.

Advanced topics in commercial and research oriented databases systems including catalogs, physical storage techniques, query processing, optimization, transaction management, concurrency control, disaster recovery, security, integrity, extended data types, triggers and rules, distributed databases, warehouses and parallelism.

In this course, students will be introduced to principles and current technologies of computer-based multimedia systems. They will study current media types (images, video, audio, graphics etc) and how they are used to create multimedia content. Issues in effectively representing, processing, and compressing multimedia data will be addressed. The students will be familiarized with the range of tools used in creating computer-based multimedia.

This course will cover the basic design and principles of operating system design and implementation. Concurrent processes, inter-process communication, job and process scheduling; deadlock and race conditions. Issues in memory management (virtual memory, segmentation, and paging) and auxiliary storage management (file systems, directory structuring, and protection mechanisms). Students complete several small projects that exercise their understanding of the material presented in class.