CSC

US and relevant international health and data security and privacy laws/regulations, HIPAA and HITECH compliance for EHR software and medical devices, federal and state patient privacy and health data access rights, electronic transmission of health data, health insurance, FDA rules and regulations, unauthorized access, vulnerabilities, unsecured wireless access, inadequate encryption, authentication failures, and other access control vulnerabilities, security risk assessment, privacy and security gaps in health information exchanges, federal and state privacy breach notification laws and related civil and criminal penalities, and successful security compliance audit and management strategies.

Fundamental strategies for algorithm design; mathematical and empirical techniques for analysis of nonrecursive and recursive algorithms, with applications such as sorting, searching, string processing and graphs; NP-complete problems and approximation algorithms.

Organization, characteristics, constructs, and design principles of programming languages; syntax, semantics, and pragmatics; language implememtation issues; different programming paradigms such as imperative, functional, object-oriented, and logic programming.

Modern database systems, including relational and NoSQL systems. Emphasize practical knowledge while covering the essential theory design; query lanquages; security; transactions. Focus on both theory and practice.

Automata theory: deterministic and non-deterministic finite automata, pushdown automata, regular languages, context-free grammars, pumping lemma. Computability and recursion theory: Turing machines and their variations, decidability and recursive enumerability, mapping reducibility and Turing reducibility, undecidability of the halting problem, logical theories and Godel's incompleteness theorem. Complexity theory: time complexity, space complexity, major open problems on computational complexity. Corequisite: CSC 8301 or degree program in mathematics.

Study of algorithms and systems that can learn without being explicitly programmed. Topics include: clustering, classification, prediction, supervised learning, unsupervised learning, decision trees, support vector machines, random forests, regression, dimensionality reduction, neural networks, deep learning, and probabilistic graphical models.

An introduction to software engineering covering development life cycle models, requirements analysis and specification design concepts and methods, testing, maintenance, CASE tools and management concerns. Additional topics may include reuse metrics, experimentation, reengineering, development environments, and standards. The student may be required to write a research paper and/or give an in-class presentation.

The advancement of embedded processes and sensor networks that have made the IOT feasible. Topics include: Introduction, Domains of application, IOT VS M2M, IOT Management, Protocols, Design Methodologies, Hands on Design using Raspberry Pi and Python, Reviewing servicers and clouds, and data analytics.

Advanced elective study of topics of current interest and importance in the computer field. This may be retaken for a different topic.