Graduate Catalog 2011-2013 - SUNY Institute of Technology

Course Descriptions
COURSE DESCRIPTIONS
CS 518 Topics in Software Engineering (3)
Topics will vary from offering to offering. In-depth development of topics
reflecting current research interest of faculty. Prerequisite: as listed by instructor.
CS 520 Computer Architecture (3)
Core principles underlying current and future machines are discussed in
quantitative terms and illustrated with current leading architectures. Review
of instruction set architectures, pipelining and cache sequences. Issues of
power, circuit costs, reliability and availability are introduced. Exploiting
instruction-level parallelism in processors including superscalar execution,
branch prediction, dynamic scheduling and relevant compiler enhancements
are considered. Organization and performance of symmetric and distributed
memory architectures: SIMD/MIMD systems, interconnection networks,
synchronization and cache coherence. Knowledge of Machine Structures,
Discrete Structures assumed.
CS 521 Operating Systems (3)
Exploration and discussion of major developments in operating systems
technologies from analysis and modeling aspects covering both centralized
and distributed architectures. Topics include: concurrency and interprocess
communication, memory management issues, file systems and I/O, multimedia
operating systems, multiprocessors, distributed systems, and research foci in
operating systems area. Prerequisite: CS330, or its equivalent.
CS 522 Computer Networks (3)
Analysis, modeling and evaluation of computer networks with Internet as the
infrastructure base. Topics include: layered models and protocol suites, unicast/
multicast routing, network management, traffic measurement and analysis,
mobile networking, router architecture, network-aware applications, content
dissemination systems, network security and performance issues. Knowledge
of discrete structures is assumed.
CS 523 Parallel Computing (3)
Techniques for programming parallel computers; trends in parallel and
distributed computing; shared memory and message passing architectures;
parallel algorithms design; synchronization; parallel algorithm performance
analysis; interconnection network topologies; parallel computing languages.
Prerequisite: Knowledge of machine structures and data structures is assumed.
CS 524 Distributed Systems (3)
This course concerns distributed multiprocessor systems in their fullest
scope. It considers both the functional and analytical structures of specialized
processors performing portions of the same task, nonspecialized processors
with limited number of states sharing a common memory, and multicomputers
geographically distributed but linked through a communications network. It
provides a foundation to evaluate the economics and feasibility of distributed
systems. Prerequisite: Knowledge of machine structures.
CS 528 Topics in Systems & Architecture (3)
Topics will vary from offering to offering. In-depth development of topics
reflecting current research interest of faculty. Prerequisite: as listed by instructor.
CS 530 Algorithms & Complexity (3)
There are three questions asked of any algorithm: Is it correct? How fast is
it? Can it be done better? A survey of the main techniques and methodologies
for designing efficient algorithms as well as the class of problems which are
currently considered intractable. Ways to cope with intractability are considered
including approximation, randomized algorithms, and local search heuristics.
Quantum computing and algorithms are introduced as well. Knowledge of
discrete structures, data structures is assumed.
CS 531 Automata, Computability and Formal Languages (3)
The stress in this course is on formal models of computation and the development
of students’ skills in utilizing rigorous concepts and definitions in computing
environments to analyze broad classes of problems situations. Classical concepts
from theoretical computer science (such as state minimization, formal languages
and their acceptors, and the theory of computable functions) will be reviewed
and /or developed. Knowledge of discrete structures is assumed.
and infrastructure, digital signatures and authentication, elliptic curve
cryptography, automata-theoretic and shift-register models of security systems,
analog security systems. Knowledge of discrete structures and data structures
required.
CS 538 Topics in Algorithms (3)
Topics will vary from semester to semester. In-depth development of topics
reflecting current research interest of faculty. Sample of topics in the Algorithms
& Theory area include: Algorithmic Game Theory, Computational Aspects
of Evolution, Information Theory, Coding Theory, Randomized Algorithms,
Combinatorial Optimization, Steganography and Digital Watermarking,
Quantum Computing and Algorithms.
CS 540 Artificial Intelligence (3)
Models of knowledge and learning representation and techniques of intelligent
problem solving. Topics include crisp and fuzzy rule-based and logic models,
constraint satisfaction problems, probabilistic graphical models, state-based
search, evolutionary population-based optimization, non-evolutionary
population-based techniques, sampling-based methods, neural networks,
supervised learning, unsupervised learning, reinforcement learning, planning,
integrative general intelligence architectures. Knowledge of discrete structures
is assumed.
CS 541 Database Systems (3)
Techniques and methodologies for the design and implementation of databased
information systems. Topics include database design, modeling and
management, database query design and implementation languages, physical
structures and algorithms for storage and access, data mining models, complex
event processing, control of data access, security and integrity, compliance with
laws and regulations for databases. Knowledge of discrete structures is assumed.
CS 542 Machine Learning (3)
Survey of basic concepts and techniques in machine learning. Topics include:
supervised vs. unsupervised learning, classification and regression, neural
networks, decision trees, kernel methods, lazy learners, ensemble learning,
clustering methods, Bayesian classifiers, evolutionary learning, reinforcement
learning and learning casual models. Knowledge of discrete and data structures
assumed.
CS 543 Introduction to Systems Theory (3)
This course develops a conceptual basis and techniques for the study of
systems and system properties useful in all areas of computer science.
Some of the properties covered are: behavior, state, dynamics, organization,
structure, hierarchy, feedback regulation and control, complexity, information,
communication, and performance. The course also develops a number of
examples and emphasizes the ability to use the abstract systems concepts
to model and study information processing systems. Knowledge of discrete
structures is assumed.
CS 548 Topics in Artificial Intelligence and Modeling (3)
Topics will vary from offering to offering. In-depth development of topics
reflecting current research interests of faculty. Prerequisite: as listed by
instructor.
CSC 580 Computer and Robotic Vision (3)
This course is designed to give the student an insight into the intrinsic image
information and the internal model of vision systems. Classification of
objects is performed by extracting linear curves and regions in images, using
boundary information, texture analysis and 3D scene analysis. Geometric
and relationship structures involving complex symbolic descriptions of image
and world structures are studied and various applications are introduced.
Cross-listed with MST 580.
CS 591
Independent Study (variable credit)
CS 598 Project (3)
CS 599
Thesis (1-6 credits)
CS 532 Cryptography and Data Security (3)
Algorithms and protocols for confidentiality, integrity, authentication and
non-repudiation of stored and transmitted data. Topics include classical
cryptographic methods, streams and block ciphers, Shannon’s entropy
and perfect secrecy, cryptanalysis, substitution permutation and Feistel
networks, the Data Encryption and Advance Encryption Standards, hash
functions and data integrity, secret sharing schemes, public key cryptosystems
CS 600 Colloquia in Computer Science (3)
Speakers from fields in computing and its applications present their current
research activities and findings. Students are required to attend a designed
number of colloquia each semester and to write reaction papers to those
presentations in areas of their interest. May be taken repeatedly, but it does
not count toward the 33 credit hour requirement for the M.S. degree.
52 Graduate Catalog 2011-2013