Computer Science (CSEN)

One or more advanced topics. May be repeated when topic changes. (Credit may not be obtained for both CSEN 5303 and EEEN 5303 courses if the topic is the same.)

Introduces the design principles of modern computers. The topics include RISC and CISC architecture, interconnection networks, multiprocessors and multicomputer systems, dataflow and systolic arrays, future outlook for architectures and the basics of parallel algorithms. (Credit may not be obtained in both CSEN 5304 and EEEN 5304.)

Designed for project option students and requires completion of research project. Prerequisite: departmental approval. May be repeated for a maximum of 6 semester hours.

Designed for thesis option students. The course requires completion of thesis research. Prerequisite: departmental approval. May be repeated for maximum of 6 semester hours.

This course introduces the structure of a compiler and the various techniques used for designing a compiler. Topics include grammars, parsing methods, implementation details and translator writing systems.

Basic concepts and architecture of database systems, ER model, relational model, relational algebra, SQL, ER-to-rational mapping, functional dependencies normalization, database design process, object-oriented database. Distributed database. Prerequisite: graduate standing in computer science or another engineering discipline.

Fundamental concepts of intelligent computer systems, knowledge representation, logical reasoning, search strategies, game theory, Bayesian networks, neural networks, reinforcement learning, natural language processing, and current research in AI area.

Operating systems principles; procedures and their implementation; protection, concurrent, cooperating and communicating processes; storage management; resource allocation; scheduling; file systems; and system design issues.

The International Standards Organization (ISO) Open Systems Interconnection (OSI) model as a framework for the study of computer communication networks. Data communication. Functions and protocols of physical layer, medium access sublayer, link layer, network layer and transport layer. Case studies. ISDN. Prerequisite: graduate standing in computer science or electrical engineering.

Covers development life-cycle models, inspection process, software quality metrics, testing, validation metrics, estimation and scheduling. Prerequisite: graduate standing in engineering.

Types of data, similarity and distance functions, data preprocessing, sampling, feature selection, discretization, attribute transformation, classification and prediction methods, decision trees, support vector machines, regressions, classification rules, Bayesian learning, neural networks, association rule mining, sequential pattern mining, cluster analysis, anomaly detection.

Fundamental concepts of machine learning including supervised and unsupervised learning, deep learning, probabilistic models, and reinforcement learning; covers key algorithms such as decision trees, neural networks, support vector machines, prototype methods and nearest neighbors, ensemble learning, and undirected graphical models; algorithm design, model evaluation, and optimization techniques.

Characteristics of systems and techniques used in real time computer applications. Scheduling theory, verification and design techniques including simulation and probabilistic models. Prerequisite: graduate standing.

Introduction of the design and analysis of computer algorithms. Topics include asymptotic efficiency; a survey of useful algorithms for sorting, information retrieval, and graphs; paradigms for algorithm design; and a brief introduction to complexity classes including NP. Prerequisite: graduate standing.

Principles of parallel algorithm design: task decomposition, synchronization, load balancing, computational dependencies; parallel computing architectures; collective communications; analyzing parallel algorithms; scalability, efficiency, speedup; parallel algorithms for fundamental problems such as sorting; shared-memory programming; distributed-memory programming; hybrid programming; message passing interface; GPU architecture and programming; transactional memory.

Cloud computing, services, and technologies for hosting, storing, and processing data on the cloud. Fundamentals of cloud computing using cloud services, such as cloud servers, databases, and data warehouses, along with principles and architectural foundations upon which cloud computing is based. Software design and implementation strategies that support the integration and exploitation of cloud-based resources, integration of cloud infrastructure into the design of software systems, security considerations associate with cloud computing, such as the use of public, private, and hybrid cloud resources.

Introduction to data science and data analytics programming in Python using libraries such as NumPy for computational array operations, SciPY for scientific and numerical computing, Pandas for data analysis and manipulation; an overview of techniques and methods for loading datasets, formatting data, feature analysis, and processing large amounts of data; collecting, pruning, munging, analyzing, visualizing, and processing data; building machine learning models, training them with data, testing the models to evaluate their performance, and using the models to make decisions or predictions.

Methods and tools used to analyze, visualize, and derive insights from complex data; data wrangling, statistical analysis, predictive modeling, data-driven decision making; applications in machine learning, natural language processing, and big data analytics; data analysis lifecycle, data acquisition and cleaning, model deployment, and result interpretation.

Includes a review of network architectures, perceptron, linear networks, back-propagation and radial basis networks. A real-time laboratory experience in seeing the application of neural networks. Prerequisite: graduate standing in Computer Science. (Credit may not be obtained in both CSEN 5350 and EEEN 5350.)

Computational and statistical algorithms in Python and Biopython for bioinformatics and biological applications. Computational tools for analyzing and modeling biological sequences of genes and proteins stored in databases in the GenBank, FASTA, and PDB formats. Gene and protein modeling, design, visualization, optimization, structure prediction, sequence analysis, sequence similarity search using BLAST, parsing, analyzing, transcribing and translating gene or protein sequences.

Design and development of software for mobile platforms including: user interface design, user experience factors, processes, threading, multitasking, database integration, application lifecycles, resource constraints in mobile environments, app security, cloud and network integration.

Individual or group research on advanced problems conducted under the supervision of a faculty member. Maximum credit 8 semester hours.

Courses offered under this Special Topics denomination concentrate on themes not present in the current EECS curriculum, or can also be offered to strengthen and provide further depth of study in important areas of computer science. Topics vary to reflect new developments and interests on emerging areas of computer science, such as cryptography, the Internet of Things, and wireless sensor networks, to name a few. May be repeated when topic changes.