Concepts and properties of algorithms; programming exercises with emphasis on good programming habits. Credit may not be received for both CSCE 500 and CSCE 145. Open to all majors. May not be used for major credit by computer science and engineering majors.

System software such as command language interpreters, client-server applications, debuggers; mail systems, browsers, macroprocessors, and revision control systems; file systems, processes, threads, and interprocess communication.

Measuring, modeling, analyzing, and predicting performance of computer systems and networks; bottleneck analysis; Markovian queuing systems and networks; use of operational and probabilistic models.

Design methodology; processor design; computer arithmetic: algorithms for addition, multiplication, floating point arithmetic; microprogrammed control; memory organization; introduction to parallel architectures.

Computer networks and communication protocols; socket programming; interprocess communication; development of network software; case studies.

Structure, design, and analysis of computer networks; ISO/OSI network architecture.

Structure, design, and analysis of computer networks; ISO/OSI network architecture.

Fundamental principles and techniques of ethical hacking, including penetration testing life cycle, planning and scoping, identifying targets and goals, active and passive reconnaissance, enumeration and scanning, exploitation, post-exploitation, and results reporting.

Database management systems; database design and implementation; security, integrity, and privacy.

Threats to information resources and appropriate countermeasures. Cryptography, identification and authentication, access control models and mechanisms, multilevel database security, steganography, Internet security, and intrusion detection and prevention.

Cooperative information systems and service-oriented computing. Techniques for achieving coordinated behavior among a decentralized group of information system components. Distributed databases, multiagent systems, conceptual modeling, Web services, and applications.

Techniques for design and implementation of compilers, including lexical analysis, parsing, syntax-directed translation, and symbol table management.

Functional programming as a paradigm. History of functional languages from Lisp to the present. Programming in Haskell, ML, or similar language at an intermediate level: recursive and higher-order functions, list comprehensions, types and classes, monads, lazy evaluation, reasoning about programs.

Development of mobile applications, including user interface design for mobile, local and cloud data storage techniques, and application architectures.

Object-oriented methods and tools for application programming with graphically interactive operating systems.

Construction of software systems resistant to vulnerabilities and attacks. Cryptographic tools. Language, operating system, and network security. Case studies. Development of best practices through programming assignments.

Basic theoretical principles of computing as modeled by formal languages and automata; computability and computational complexity.

Design and development of computer games, with emphasis on the technologies used. Hands-on development of computer games.

Concepts, algorithms and tools for important problems in Bioinformatics, including nucleotide and amino acid sequence alignment, DNA fragment assembly, phylogenetic reconstruction, and protein structure visualization and assessment.

Hands-on introduction in Python to the analysis of neuroscience data (human neuroimaging and cellular electrophysiology), including various aspects such as data wrangling, statistics, classification, and visualization.

Design of secret codes for secure communication, including encryption and integrity verification: ciphers, cryptographic hashing, and public key cryptosystems such as RSA. Mathematical principles underlying encryption. Code-breaking techniques. Cryptographic protocols.

Interpolation and approximation of functions; solution of algebraic equations; numerical differentiation and integration; numerical solutions of ordinary differential equations and boundary value problems; computer implementation of algorithms.

Computer simulation of real systems; principles of system organization; random number generation; programming exercises in a simulation language.

Parallel algorithms; scientific visualization; techniques for solving scientific problems.

Graphics hardware; graphics primitives; two-dimensional and three-dimensional viewing; basic modeling.

Scientific visualization tools as applied to sampled and generated data; methods for data manipulation and representation; investigation of visualization techniques.

Architecture and interconnection of parallel computers; parallel programming models and applications; issues in high-performance computing; programming of parallel computers.

Foundational techniques in multidisciplinary software development, specifically of applications designed to present sensitive, sometimes controversial, materials in ways to engender empathic awareness of the interactor.

Interdisciplinary approach to interaction design, user-centered design, human abilities, survey development, experimental study methodology, heuristic evaluations, usability testing, universal design, and accessibility.

Design and application of robotic systems; emphasis on mobile robots and intelligent machines.

Text and natural language processing; formal models and data structures appropriate for text processing; selected topics in computational linguistics, stylistics, and content analysis.

Heuristic problem solving, theorem proving, and knowledge representation, including the use of appropriate programming languages and tools.

AI Trust – responsible/ethical technology, fairness/ lack of bias, explanations (XAI), machine learning, reasoning, software testing, data quality and provenance, tools and projects.

Normative approaches to uncertainty in artificial intelligence. Probabilistic and causal modeling with Bayesian networks and influence diagrams. Applications in decision analysis and support. Algorithms for probability update in graphical models.

Introduction to artificial neural networks, neural network topologies, neural network learning paradigms, training rules, and applications across supervised, unsupervised, and reinforcement learning tasks.

Design and implementation of machine learning systems, Deep learning systems stack, machine learning platforms, scalable and distributed machine learning.

Foundational techniques and tools required for data science and big data analytics. Concepts, principles, and techniques applicable to any technology and industry for establishing a baseline that can be enhanced by future study.

Theory and application of machine learning. Current mainstream programming libraries such as in Python, and implementation of regression, clustering, principal components, and linear discriminant analysis. Detailed coverage of methods such as random forests, support vector machines, and k-nearest neighbors. Introduction to neural networks.

Reading and research on selected topics in information technology. Course content varies and will be announced in the schedule of courses by title. May be repeated for credit as topics vary.

Strategic management and use of information systems in organizations.

Design techniques for logic systems; emphasis on higher-level CAD tools such as hardware description languages and functional modeling.

VLSI design process models, introduction to EDA tools, HDL modeling and simulation, logic synthesis and simulation, benchmark design projects.

Design of VLSI circuits, including standard processes, circuit design, layout, and CAD tools. Lecture and guided design projects.

Operating system organization and interactive processing systems, multiprogramming systems, process management, task scheduling, resource control, deadlocks.

Architecture of high-performance computers, including array processors, multiprocessor systems, data flow computers, and distributed processing systems.

Artificial Intelligence of Things (AIoT) Systems, Edge AI Hardware, advanced concepts in brain-inspired computing architectures, neuromorphic computing algorithms and hardware, spiking neural networks (SNN), in-memory computing, processing-in-memory architectures, edge computing and neuromorphic computing applications.

Analysis of security threats in TCP/IP networks. Design of safeguards. Coverage of security threats at each of the OSI layers. Application of cryptographic protocols for secure communication across a network.

Design of more reliable systems through the application of reliability theory and models; reliability modeling; design techniques; testing; and requirement specifications.

Evaluation of computer system performance and reliability using reliability block diagrams, fault trees, reliability graphics, queuing networks, Markov models, and Markov reward models.

Problems of real-time computer applications in process control or similar areas; task scheduling; real-time operating systems; advanced interrupt structures; memory management techniques.

This course focuses on the security and privacy issues associated with wireless networks. Various attacks against wireless networks and their defense strategies will be analyzed. Students are able to embark in research of wireless network security.

Components of a database management system; implementation issues; query optimization; file organization; file organizations’ transaction management; fault recovery; security; system performance.

Database design methodologies and tools; data models; implementation languages; user interfaces.

Structure, design, evaluation, and use of information retrieval systems; algorithms and mathematical models for information retrieval; storage and retrieval of textual data in information systems.

Current trends and challenges in information warfare. High-level analysis of information warfare threats, like cyber terrorism, espionage, Internet fraud, intelligence activities, cyber ethics, and law enforcement.

Approaches for specifying programming language semantics, including operational, axiomatic, and denotational specification.

Current practices and research in software development, requirements definition, design, program testing and reliability, maintenance, and management.

Personal, team, and organizational software processes; personal and organizational maturity; application of software process and management concepts during software development, primarily at the individual level.

Structural organizations for software systems as collections of interconnected components: formal models and languages; design tools and guidelines.

Elicitation, analysis, and validation of software requirements, specification of software systems including formal specification methods; CASE tools.

Fundamentals of object-oriented technology; object modeling of structure, function, and time-dependent behavior; system analysis and design.

Object-oriented programming paradigm, including encapsulation, inheritance, reusable classes, object classification, specialization, and message passing; case studies and applications.

Structural and functional techniques for testing software; code inspection, peer review, test verification and validation; statistical testing methods; preventing and detecting errors; testing metrics; test plans; formal methods of testing.

Algorithm design techniques; algorithms and data structures for sets and graphs; time and space complexity; sorting and searching; NP-complete problems.

Formal models of computation, including finite state automata, Turing machines, recursive functions, formal grammars, and abstract complexity theory.

Application of probability theory and stochastic processes to analyze the dynamic behavior of engineering systems.

Numerical solution of equations and systems of linear equations, polynomial approximation, difference calculus, solution of ordinary and partial differential equations, least squares and sets of orthogonal polynomials, Gaussian quadrature.

Continuation of CSCE 760.

Concepts and techniques for digital image processing; emphasis on low-level processes that analyze discrete images at the pixel level.

Fundamentals of quantum information theory and quantum communications. Topics include: Postulates of quantum mechanics, classical information and entropy, compression of classical information and classical typical sets, quantum entropy and quantum relative entropy, quantum states discrimination, Schumacher's theory of quantum compression and quantum typical subspace, communicating classical information using quantum channels, the Classical Capacity Theorem of a quantum channel.

Cross-listed course: MATH 764, PHYS 764

Graphics data structures, graphics languages, modeling, raster displays, 3-D shading, hidden surface algorithms.

Visualization techniques for scientific computing; interactive steering of calculations; animation and rendering techniques for multivariate data analysis.

Principles for the design of systems supporting effective human-computer interaction; interaction styles; displays and interactive devices; user assistance; system design and evaluation.

Bayesian classifiers; optimal risk schemes; error rates; numerical methods; implementation; architectures.

Theoretical concepts and algorithmic tools currently utilized in the field of protein folding such as Xplor-NIH and ROSETTA are presented. Participants are enabled to embark in research of protein folding.

Computational models for the analysis and synthesis of natural language; representations for syntax and semantics; applications to text-to-speech conversion, speech recognition, and language understanding.

A/D conversion, digital filters, discrete Fourier transform and FFT, acoustics of speech, and synthesis and recognition of speech.

Design and operation of robot systems; dynamics, control, and motion trajectories of manipulators; visual, auditory, and tactile sensing systems; planning and learning.

Theory and practical implementation of reinforcement learning methods with deep neural networks. Markov decision processes, policy iteration, value iteration, q-learning, SARSA, deep neural networks, approximate value iteration, deep q-networks, policy gradients, model-based reinforcement learning, multi-armed bandits, partial observability.

Representation techniques and languages for symbolic knowledge, including predicate calculus, frame-based systems, and terminological systems; computer reasoning using these systems.

Expert system domains, knowledge representation techniques, inference engines, and knowledge acquisition methods.

Coordinated problem solving by multiple knowledge systems.

Mathematical foundations of biological and artificial neural networks, supervised and unsupervised systems, applications.

Fundamentals of quantum information processing, including quantum computation, quantum cryptography, and quantum information theory. Topics include: the quantum circuit model, alternative models, qubits, unitary operators, measurement, entanglement, quantum algorithms for factoring and search, quantum cryptographic key distribution, simulation of physical systems, error-correction and fault-tolerance, information capacity of quantum channels, complexity of quantum computation, near-term implementations, quantum supremacy and quantum advantage.

Principles of fuzzy set theory, fuzzy relations, and fuzzy logic; fuzzy “if-then” rules.

Reading and research on selected topics in information technology. Course content varies and will be announced in the schedule of courses by title. May be repeated for credit as topics vary.

Technical writing and presentations in major computing research areas; ethics in research and writing.

Work. Written report required. Not auditable.

Individual research to be arranged with the instructor.

Individual research to be arranged with the instructor.

Study security threats and prevention/detection/response techniques on the Internet, including hackers, masqueraders, information spoofing, sniffing, and distribution of damaging software. Security analysis of Web applications.

Security mechanisms of distributed software systems, including cryptographic applications. Secure multiparty computation, group-based cryptography, and security mechanisms for emerging distributed architectures.

Contemporary computer communication protocols and network architectures.

VLSI system design automation, hardware description language-based design, multi-methodology design, and introduction to HDL support tools.

Custom design methodology design rules, stick notation, logic synthesis, and circuit layout; symbolic layout languages; introduction to CAD tools.

Architecture, design, and implementation of distributed database managements systems; data fragmentation, replication, and allocation; query processing and transaction management; distributed object database management systems.

Information processing techniques and mathematical tools to assemble, access, and analyze data for decision support and knowledge discovery.

Security threats to database systems. Access control models, multilevel security, integrity, Web-based databases, and data inference problem. Formal models of multilevel security, confidentiality versus availability and integrity.

Strategies for achieving coordinated behavior among a heterogeneous group of information system components; world-wide information networks and applications in health care, logistics, telecommunications, and manufacturing automation.

Reliability and safety of computer-intensive systems; software reliability models and analysis; operational profiles; hazard analysis using fault trees and event trees; formal verification of safety-critical systems.

Definitions of algorithms and formal models of computation; concepts of space and time; synthesis and analysis of algorithms for sorting, search graphs, set manipulation and pattern matching; NP-complete, and intractable problem.

Formal techniques applied to computer security, including formal specification language for security properties, security analysis utilities, domain-specific security concerns, and case studies of formally verified secure systems.

Input and display devices, data structures, architectures, primitives, and geometrical transformations appropriate to computer graphics, parametric surfaces.

Scene segmentation using texture, color, motion; representation of 2-D or 3-D structures; knowledge-based vision systems.

Feature nomination, selection, extraction, and evaluation; deterministic, stochastic, and fuzzy models for classifier design; parameter estimation; error rate estimation; clustering and sequential learning.

Fundamentals of machine learning including rote learning, learning from examples, learning from observations, and learning by analogy; knowledge acquisition for expert systems.