CS 500 Logic in Computer Science |
3 Credits |
Propositional and first-order logic (soundness and
completeness, incompleteness, undecidability, etc.).
Logical issues in computer science (decision procedures,
formal systems, definability, etc.).
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2023-2024 |
Logic in Computer Science |
3 |
Fall 2021-2022 |
Logic in Computer Science |
3 |
Spring 2017-2018 |
Logic in Computer Science |
3 |
Fall 2016-2017 |
Logic in Computer Science |
3 |
Fall 2015-2016 |
Logic in Computer Science |
3 |
Fall 2014-2015 |
Logic in Computer Science |
3 |
Spring 2010-2011 |
Logic in Computer Science |
3 |
Spring 2007-2008 |
Logic in Computer Science |
3 |
Spring 2006-2007 |
Logic in Computer Science |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 502 Automated Reasoning |
3 Credits |
Formal principles, and algorithms for reasoning
about knowledge represented in a logical language
(e.g., methods used by the state-of-the-art SAT solvers,
QBF solvers, and theorem provers, algorithms for
knowledge compilation, logical entailment, and model
counting), and their applications in computer science
(e.g., prediction, diagnosis and testing, planning, model
checking, automated theorem proving, constraint
satisfaction).
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2020-2021 |
Automated Reasoning |
3 |
Spring 2012-2013 |
Automated Reasoning |
3 |
Spring 2006-2007 |
Automated Reasoning |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 503 Theory of Computation |
3 Credits |
Turing machines; recursive numbers and Turing computability;
solvability and unsolvable problems; concepts of and results
on computational complexity; some NP
complete problems
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2023-2024 |
Theory of Computation |
3 |
Spring 2022-2023 |
Theory of Computation |
3 |
Spring 2021-2022 |
Theory of Computation |
3 |
Spring 2018-2019 |
Theory of Computation |
3 |
Spring 2017-2018 |
Theory of Computation |
3 |
Spring 2016-2017 |
Theory of Computation |
3 |
Spring 2015-2016 |
Theory of Computation |
3 |
Spring 2013-2014 |
Theory of Computation |
3 |
Spring 2012-2013 |
Theory of Computation |
3 |
Spring 2011-2012 |
Theory of Computation |
3 |
Spring 2010-2011 |
Theory of Computation |
3 |
Spring 2009-2010 |
Theory of Computation |
3 |
Spring 2006-2007 |
Theory of Computation |
3 |
Spring 2005-2006 |
Theory of Computation |
3 |
Spring 2004-2005 |
Theory of Computation |
3 |
Fall 2001-2002 |
Theory of Computation |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 504 Knowledge Representation and Reasoning |
3 Credits |
Mathematical foundations of various knowledge representation
and reasoning formalisms (e.g., classical logic,answer
set programming, action languages, situation calculus,
description logic, constraint programming),
and their applications to computer science and other
sciences (e.g., commonsense knowledge
representation, belief/theory revision/update, Semantic
Web, graph theory, planning, diagnosis,
VLSI design, historical linguistics, computational biology,
biomedical informatics).
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2022-2023 |
Knowledge Representation and Reasoning |
3 |
Spring 2019-2020 |
Knowledge Representation and Reasoning |
3 |
Fall 2017-2018 |
Knowledge Representation and Reasoning |
3 |
Fall 2015-2016 |
Knowledge Representation and Reasoning |
3 |
Fall 2011-2012 |
Knowledge Representation and Reasoning |
3 |
Fall 2010-2011 |
Knowledge Representation and Reasoning |
3 |
Fall 2008-2009 |
Knowledge Representation and Reasoning |
3 |
Fall 2007-2008 |
Knowledge Representation and Reasoning |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 506 Cognitive Robotics |
3 Credits |
Kinematic and dynamic modeling of robots.
Architectures for robot control. World maps
and localization. Object recognition. Manipulation
and path planning. Human-Robot interaction.
Artificial Intelligence planning. Sensing
and monitoring. Diagnosis. Robotic learning.
Representation and reasoning formalisms and
algorithms. Methods for coupling high-level
reasoning with low-level control.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2016-2017 |
Cognitive Robotics |
3 |
Spring 2012-2013 |
Cognitive Robotics |
3 |
Spring 2011-2012 |
Cognitive Robotics |
3 |
Spring 2009-2010 |
Cognitive Robotics |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 507 Cryptography |
3 Credits |
This is an introductory course on cryptography. Topics
include: Classical cryptosystems, basics of number theory,
symmetric key cryptography (stream and block ciphers), hash
functions, public key cryptosystems (RSA, discrete logarithm
based algorithms, and elliptic curve cryptography (ECC)),
digital signatures, implementation issues, secure key
establishment techniques, secret sharing, and zero-knowledge
proof.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2023-2024 |
Cryptography |
3 |
Fall 2022-2023 |
Cryptography |
3 |
Fall 2021-2022 |
Cryptography |
3 |
Fall 2020-2021 |
Cryptography |
3 |
Fall 2019-2020 |
Cryptography |
3 |
Fall 2018-2019 |
Cryptography |
3 |
Fall 2017-2018 |
Cryptography |
3 |
Fall 2015-2016 |
Cryptography |
3 |
Fall 2014-2015 |
Cryptography |
3 |
Fall 2012-2013 |
Cryptography |
3 |
Fall 2010-2011 |
Cryptography |
3 |
Fall 2009-2010 |
Cryptography |
3 |
Fall 2008-2009 |
Cryptography |
3 |
Fall 2006-2007 |
Cryptography |
3 |
Fall 2004-2005 |
Cryptography |
3 |
Fall 2002-2003 |
Cryptography |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 510 Formal Methods for Reliable Digital Systems |
3 Credits |
This course introduces the formal verification and testing
methods for digital systems, which includes both software
and digital hardware. In the first part of the course,
formal testing based on finite state machine representation
of digital systems is studied. Black box and white box
testing methods are also covered. In the second part of the
course, model checking is introduced as a formal approach
for verification. The practical problems of model checking,
and some complexity relief techniques are also discussed.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2023-2024 |
Formal Methods for Reliable Digital Systems |
3 |
Spring 2019-2020 |
Formal Methods for Reliable Digital Systems |
3 |
Fall 2018-2019 |
Formal Methods for Reliable Digital Systems |
3 |
Fall 2016-2017 |
Formal Methods for Reliable Digital Systems |
3 |
Fall 2015-2016 |
Formal Methods for Reliable Digital Systems |
3 |
Fall 2013-2014 |
Formal Methods for Reliable Digital Systems |
3 |
Fall 2012-2013 |
Formal Methods for Reliable Digital Systems |
3 |
Spring 2010-2011 |
Formal Methods for Reliable Digital Systems |
3 |
Spring 2009-2010 |
Formal Methods for Reliable Digital Systems |
3 |
Fall 2009-2010 |
Formal Methods for Reliable Digital Systems |
3 |
Spring 2008-2009 |
Formal Methods for Reliable Digital Systems |
3 |
Spring 2007-2008 |
Formal Methods for Reliable Digital Systems |
3 |
Spring 2006-2007 |
Formal Methods for Reliable Digital Systems |
3 |
Spring 2005-2006 |
Formal Methods for Reliable Digital Systems |
3 |
Spring 2004-2005 |
Formal Methods for Reliable Digital Systems |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 512 Machine Learning |
3 Credits |
This is an introductory machine learning course that will
aim a solid understanding of the fundamental issues in
machine learning together with several ML techniques such
as decision trees, k-nearest neighbor, Bayesian classifiers,
neural networks, linear and logistic regression,
clustering, SVM and ensemble
techniques.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2023-2024 |
Machine Learning |
3 |
Fall 2022-2023 |
Machine Learning |
3 |
Fall 2021-2022 |
Machine Learning |
3 |
Fall 2020-2021 |
Machine Learning |
3 |
Fall 2019-2020 |
Machine Learning |
3 |
Fall 2018-2019 |
Machine Learning |
3 |
Fall 2017-2018 |
Machine Learning |
3 |
Fall 2015-2016 |
Machine Learning |
3 |
Fall 2014-2015 |
Machine Learning |
3 |
Fall 2013-2014 |
Machine Learning |
3 |
Fall 2012-2013 |
Machine Learning |
3 |
Fall 2011-2012 |
Machine Learning |
3 |
Fall 2010-2011 |
Machine Learning |
3 |
Fall 2009-2010 |
Machine Learning |
3 |
Fall 2007-2008 |
Machine Learning |
3 |
Spring 2005-2006 |
Machine Learning |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 513 Topics in Natural Language Processing |
3 Credits |
This course will cover various aspects of natural language
processing. Topics include parsing algorithms, application
of finite state methods to language processing tasks such as
morphological analysis and morphological disambiguation
statistical language processing, and applications such as
machine translation, information extraction.
|
Last Offered Terms |
Course Name |
SU Credit |
Summer 2008-2009 |
Topics in Natural Language Processing |
3 |
Fall 2007-2008 |
Topics in Natural Language Processing |
3 |
Fall 2005-2006 |
Topics in Natural Language Processing |
3 |
Fall 2004-2005 |
Topics in Natural Language Processing |
3 |
Fall 2002-2003 |
Topics in Natural Language Processing |
3 |
Fall 2001-2002 |
Topics in Natural Language Processing |
3 |
Fall 2000-2001 |
Topics in Natural Language Processing |
3 |
|
Prerequisite: __ |
Corequisite: |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 514 Network Science |
3 Credits |
Network science is a framework to analyze the
complex systems of technological, biological,
and cultural networks. This course will present
the fundamentals of networks, mathematical
toolsets to study and characterize networked
data, and develop skills for network thinking.
Special network topics such as network models,
communities, and dynamics on networks will
be presented.
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2023-2024 |
Network Science |
3 |
Spring 2022-2023 |
Network Science |
3 |
Spring 2021-2022 |
Special Topics in CS: Network Science (CS58002) |
3 |
Spring 2020-2021 |
Special Topics in CS: Network Science (CS58002) |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 515 Deep Learning |
3 Credits |
This course covers the theory and foundations of
Artificial Neural Networks (ANN) and various
ANN architectures, such as the single and multi-
layer perceptrons, Hopfield and Kohonen networks,
and deep learning architectures (convolutional
neural networks, autoencoders, restricted Boltzman
machines, recurrent networks and LSTMs, and
generative adversarial networks). Students will be
expected to develop systems for machine learning
problems from the computer vision and natural
language understanding areas.
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2023-2024 |
Deep Learning |
3 |
Spring 2022-2023 |
Deep Learning |
3 |
Spring 2021-2022 |
Deep Learning |
3 |
Spring 2020-2021 |
Deep Learning |
3 |
Spring 2019-2020 |
Deep Learning |
3 |
Spring 2018-2019 |
Deep Learning |
3 |
Spring 2002-2003 |
Neural Networks |
3 |
Spring 2000-2001 |
Neural Networks |
3 |
|
Prerequisite: (CS 512 - Masters - Min Grade D |
or CS 512 - Doctorate - Min Grade D |
or EE 566 - Masters - Min Grade D |
or EE 566 - Doctorate - Min Grade D) |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 516 Biometrics |
3 Credits |
This course is designed to cover state-of-the-art
biometric identification and verification technologies.
The topics covered will include the following:
Overview of biometrics and design of a biometric
system; fundamentals of fingerprint, iris, face,
signature, hand geometry, and voice verification
and identification technologies; multimodal
biometrics; template protection and privacy issues
in biometrics; security analysis of biometric
systems; pattern recognition techniques used
in biometric systems.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2019-2020 |
Biometrics |
3 |
Spring 2012-2013 |
Biometrics |
3 |
Fall 2011-2012 |
Biometrics |
3 |
Spring 2009-2010 |
Biometrics |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 517 Advanced Cryptography and Data Security |
3 Credits |
Information theoretic aspects of cryptography,
homomorphic encryption, lattice-based cryptography,
oblivious transfer, commitment schemes, zero-
knowledge proofs, secure two-party computation,
secure multi-party computation, electronic voting
applications, side-channel and fault attacks.
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2020-2021 |
Advanced Cryptography and Data Security |
3 |
Spring 2018-2019 |
Advanced Cryptography and Data Security |
3 |
Spring 2017-2018 |
Advanced Cryptography and Data Security |
3 |
Spring 2014-2015 |
Advanced Cryptography and Data Security |
3 |
Spring 2012-2013 |
Advanced Cryptography and Data Security |
3 |
Spring 2010-2011 |
Advanced Cryptography and Data Security |
3 |
Fall 2007-2008 |
Advanced Cryptography and Data Security |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 518 Computer Vision |
3 Credits |
This course provides a comprehensive introduction to
computer vision, starting from digital Image analysis
(filtering, image pyramids, frequency based processing,
Hough transform and invariant feature extraction),
advancing to geometry based computer vision (2D
transforms, homographies, camera models and stereo),
and ending with the presentation of state-of-the-art deep
learning based computer vision techniques
(convolutional networks, vision transformers, object
detection and semantic segmentation).
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2022-2023 |
Computer Vision |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 525 Data Mining |
3 Credits |
Data mining can be viewed as lossy data reduction
and learning techniques that are designed
to handle massive data sets containing large numbers
of categorical and numeric attributes. This course
covers topics in data mining and knowledge discovery
structured and unstructured databases such as data
integration, mining, and interpretation of patterns,
rule-based learning, decision trees, association
rule mining, and statistical analysis for discovery
of patterns, evaluation and interpretation of the
mined patterns using visualization techniques.
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2023-2024 |
Data Mining |
3 |
Spring 2021-2022 |
Data Mining |
3 |
Spring 2020-2021 |
Data Mining |
3 |
Spring 2019-2020 |
Data Mining |
3 |
Fall 2018-2019 |
Data Mining |
3 |
Fall 2017-2018 |
Data Mining |
3 |
Fall 2016-2017 |
Data Mining |
3 |
Fall 2015-2016 |
Data Mining |
3 |
Spring 2014-2015 |
Data Mining |
3 |
Spring 2012-2013 |
Data Mining |
3 |
Spring 2011-2012 |
Data Mining |
3 |
Fall 2011-2012 |
Data Mining |
3 |
Fall 2008-2009 |
Data Mining |
3 |
Fall 2007-2008 |
Data Mining |
3 |
Fall 2006-2007 |
Data Mining |
3 |
Fall 2004-2005 |
Data Mining |
3 |
Fall 2003-2004 |
Data Mining |
3 |
Spring 2002-2003 |
Data Mining |
3 |
Spring 2001-2002 |
Data Mining |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 528 Big Data Processing |
3 Credits |
To understand the big data storage and big data
processing problems that arise with the growth
of the data. To teach the tools and environments
that are necessary to deal with the problems that
come with big data. This course will give
students the hands-on ability to perform data
analysis and machine learning operations using
open source technologies on big data
environments by introducing them.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2023-2024 |
Big Data Processing |
3 |
Spring 2021-2022 |
Big Data Processing |
3 |
Spring 2020-2021 |
Big Data Processing |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 531 Parallel Processing and Algorithms |
3 Credits |
This course covers parallel computing architectures and
interconnection networks, issues such as speedup, efficiency
cost, granularity and scalability, and topics in parallel
algorithms for many important problems such as sparse and
and dense matrix operations (e.g., transposition,
matrix-vector multiplication, matrix-matrix multiplication,
solution of linear system of equations), graph problems
and other computationally intensive problems in numerical
applications.
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2023-2024 |
Parallel Processing and Algorithms |
3 |
Spring 2022-2023 |
Parallel Processing and Algorithms |
3 |
Spring 2020-2021 |
Parallel Processing and Algorithms |
3 |
Spring 2019-2020 |
Parallel Processing and Algorithms |
3 |
Spring 2018-2019 |
Parallel Processing and Algorithms |
3 |
Fall 2017-2018 |
Parallel Processing and Algorithms |
3 |
Spring 2016-2017 |
Parallel Processing and Algorithms |
3 |
Spring 2015-2016 |
Parallel Processing and Algorithms |
3 |
Spring 2014-2015 |
Parallel Processing and Algorithms |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 532 Computer and Network Security |
3 Credits |
Overview of Cryptography, Identification and Authentication,
Access Control, Operating System Security (UNIX and
Windows Environments), Key Distribution, TCP/IP Security,
IPSec, DNSSEC, WWW Security, SSL and TLS, E-mail
Security (PGP, S/MIME), PKI and certificate systems,
Viruses, Firewalls, Intrusion Detection, E-commerce Security
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2021-2022 |
Computer and Network Security |
3 |
Spring 2020-2021 |
Computer and Network Security |
3 |
Spring 2018-2019 |
Computer and Network Security |
3 |
Spring 2012-2013 |
Computer and Network Security |
3 |
Spring 2007-2008 |
Computer and Network Security |
3 |
Spring 2006-2007 |
Computer and Network Security |
3 |
Spring 2004-2005 |
Computer and Network Security |
3 |
Spring 2003-2004 |
Computer and Network Security |
3 |
Spring 2002-2003 |
Computer and Network Security |
3 |
|
Prerequisite: __ |
Corequisite: CS 532L |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 532L Computer & Network Security-Lab |
0 Credit |
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2021-2022 |
Computer & Network Security-Lab |
0 |
Spring 2020-2021 |
Computer & Network Security-Lab |
0 |
Spring 2018-2019 |
Computer and Network Security- Recitation (CS532R) |
0 |
Spring 2012-2013 |
Computer and Network Security- Recitation (CS532R) |
0 |
Spring 2007-2008 |
Computer and Network Security- Recitation (CS532R) |
0 |
|
Prerequisite: __ |
Corequisite: CS 532 |
ECTS Credit: NONE ECTS (NONE ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 534 Distributed Systems |
3 Credits |
This course focuses on the design, implementation and
management of distributed computing systems. Topics
include: naming, security, reliability, resource sharing,
and remote execution; network protocol issues above the
transport level; electronic mail; network and distributed
file systems and databases; handling transactions and
coordination of multiple machines, consistency models
and distributed semantics, fault tolerance.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2023-2024 |
Distributed Systems |
3 |
Fall 2022-2023 |
Distributed Systems |
3 |
Fall 2021-2022 |
Distributed Systems |
3 |
Fall 2020-2021 |
Distributed Systems |
3 |
Spring 2019-2020 |
Distributed Systems |
3 |
Spring 2018-2019 |
Distributed Systems |
3 |
Spring 2017-2018 |
Distributed Systems |
3 |
Spring 2015-2016 |
Distributed Systems |
3 |
Spring 2013-2014 |
Distributed Systems |
3 |
Spring 2011-2012 |
Distributed Systems |
3 |
Spring 2010-2011 |
Distributed Systems |
3 |
Fall 2008-2009 |
Distributed Systems |
3 |
Spring 2006-2007 |
Distributed Systems |
3 |
Spring 2005-2006 |
Distributed Systems |
3 |
Spring 2003-2004 |
Distributed Systems |
3 |
Spring 2002-2003 |
Distributed Systems |
3 |
|
Prerequisite: __ |
Corequisite: CS 534L |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 534L Distributed Systems - Lab |
0 Credit |
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2023-2024 |
Distributed Systems - Lab |
0 |
Fall 2022-2023 |
Distributed Systems - Lab |
0 |
Fall 2021-2022 |
Distributed Systems - Lab |
0 |
Fall 2020-2021 |
Distributed Systems - Lab |
0 |
Spring 2019-2020 |
Distributed Systems - Lab |
0 |
Spring 2018-2019 |
Distributed Systems - Lab |
0 |
Spring 2017-2018 |
Distributed Systems - Lab |
0 |
Spring 2015-2016 |
Distributed Systems - Lab |
0 |
Spring 2013-2014 |
Distributed Systems - Lab |
0 |
Spring 2011-2012 |
Distributed Systems - Lab |
0 |
Spring 2010-2011 |
Distributed Systems - Lab |
0 |
Spring 2006-2007 |
Distributed Systems - Lab |
0 |
Spring 2005-2006 |
Distributed Systems - Lab |
0 |
Spring 2003-2004 |
Distributed Systems - Lab |
0 |
|
Prerequisite: __ |
Corequisite: CS 534 |
ECTS Credit: NONE ECTS (NONE ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 535 Wireless Network Security |
3 Credits |
This course covers security and privacy issues in wireless
networks and systems, such as cellular networks,
wireless LANs, wireless PANs, mobile ad hoc networks,
vehicular networks, satellite networks, wireless mesh
networks, sensor networks and RFID systems. Security
problems of MAC and especially upper layers
will be emphasized. Attacks and proposed solutions at
several layers, authentication, key distribution
and key management, secure routing, selfish and malicious
behaviors, and secure group communication are
analyzed for applicable wireless network types. A short
overview of cryptography and wireless networking
principles will be given at the beginning of the course.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2023-2024 |
Wireless Network Security |
3 |
Fall 2020-2021 |
Wireless Network Security |
3 |
Fall 2019-2020 |
Wireless Network Security |
3 |
Fall 2018-2019 |
Wireless Network Security |
3 |
Fall 2016-2017 |
Wireless Network Security |
3 |
Fall 2014-2015 |
Wireless Network Security |
3 |
Fall 2013-2014 |
Wireless Network Security |
3 |
Fall 2012-2013 |
Wireless Network Security |
3 |
Fall 2011-2012 |
Wireless Network Security |
3 |
Fall 2010-2011 |
Wireless Network Security |
3 |
Fall 2009-2010 |
Wireless Network Security |
3 |
Fall 2008-2009 |
Wireless Network Security |
3 |
Fall 2007-2008 |
Wireless Network Security |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 539 Software Verification and Validation |
3 Credits |
This course covers some of the fundamental concepts,
methods, strategies, and techniques related to software
verification and validation. Topics included are:
software quality assurance concepts, issues, and
principles; boundary value testing; equivalence class
testing; decision table-based testing; test coverage
metrics; unit testing; path testing; control and data flow
testing; usage-based statistical testing; integration
testing; combinatorial testing; model-based testing;
regression testing; static and dynamic program analysis;
software inspections and walkthroughs; continuous
integration; problem analysis and reporting; and
program debugging.
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2020-2021 |
Software Verification and Validation |
3 |
Fall 2018-2019 |
Software Verification and Validation |
3 |
Spring 2016-2017 |
Software Verification and Validation |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 542 Software Design Patterns |
3 Credits |
This course introduces the use of design patterns
Creational, structural and behavioral patterns,
enterprise software architecture patterns,
anti-patterns, object-oriented design principles
and processes will be discussed.
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2022-2023 |
Software Design Patterns |
3 |
Spring 2021-2022 |
Special Topics in CS: Software Design Patterns (CS58005) |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 543 Computer Graphics and Visualization |
3 Credits |
This course provides a study of computer graphics
representation schemes and rendering algorithms such as
advanced methods for representing, displaying, and rendering
two- and three-dimensional scenes, general algebraic
curves and surfaces, splines, Gaussian and bump-function
representations, fractals, particle systems, constructive
solid geometry methods, lighting models, radiosity, advanced
ray-tracing methods, surface texturing, animation techniques
data visualization methods.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2023-2024 |
Computer Graphics and Visualization |
3 |
Fall 2021-2022 |
Computer Graphics and Visualization |
3 |
Fall 2020-2021 |
Computer Graphics and Visualization |
3 |
Fall 2016-2017 |
Computer Graphics and Visualization |
3 |
Spring 2014-2015 |
Computer Graphics and Visualization |
3 |
Fall 2013-2014 |
Computer Graphics and Visualization |
3 |
Fall 2012-2013 |
Computer Graphics and Visualization |
3 |
Fall 2010-2011 |
Computer Graphics and Visualization |
3 |
Fall 2008-2009 |
Computer Graphics and Visualization |
3 |
Fall 2006-2007 |
Computer Graphics and Visualization |
3 |
Fall 2005-2006 |
Computer Graphics and Visualization |
3 |
Fall 2004-2005 |
Computer Graphics and Visualization |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 546 Deep Natural Language Processing |
3 Credits |
This course studies the theory, design and implementation
of natural language processing systems which use
neural networks. Topics include word embeddings, neural
language modeling, use of CNN and RNNs for
text, seq2seq modeling, attention mechanisms, transformers,
recursive neural networks, transfer learning for NLP.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2021-2022 |
Deep Natural Language Processing |
3 |
Fall 2020-2021 |
Deep Natural Language Processing |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 549 Human Computer Interaction |
3 Credits |
This course provides students with a sound introduction to
the discipline of HCI and examines the issues of human
factors, user experience (UX), the design and test of
computer application interfaces. It focuses on the context
of designing and using of computer interfaces and covers
methodologies for obtaining and interpreting human
behaviour as it applies to the design of user interfaces.
Students will develop skills in observing and working with
users in interdisciplinary groups, identifying constraints
and trade-offs on designs within the context of use, and
using models of work and other activity as guides to
interface design.
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2023-2024 |
Human Computer Interaction |
3 |
Fall 2023-2024 |
Human Computer Interaction |
3 |
Fall 2022-2023 |
Human Computer Interaction |
3 |
Spring 2012-2013 |
Human Computer Interaction (CS545) |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 551 Graduate Seminar I |
0 Credit |
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2023-2024 |
Graduate Seminar I |
0 |
Fall 2023-2024 |
Graduate Seminar I |
0 |
Spring 2022-2023 |
Graduate Seminar I |
0 |
Fall 2022-2023 |
Graduate Seminar I |
0 |
Spring 2021-2022 |
Graduate Seminar I |
0 |
Fall 2021-2022 |
Graduate Seminar I |
0 |
Spring 2020-2021 |
Graduate Seminar I |
0 |
Fall 2020-2021 |
Graduate Seminar I |
0 |
Spring 2019-2020 |
Graduate Seminar I |
0 |
Fall 2019-2020 |
Graduate Seminar I |
0 |
Spring 2018-2019 |
Graduate Seminar I |
0 |
Fall 2018-2019 |
Graduate Seminar I |
0 |
Spring 2017-2018 |
Graduate Seminar I |
0 |
Fall 2017-2018 |
Graduate Seminar I |
0 |
Spring 2016-2017 |
Graduate Seminar I |
0 |
Fall 2016-2017 |
Graduate Seminar I |
0 |
Spring 2015-2016 |
Graduate Seminar I |
0 |
Fall 2015-2016 |
Graduate Seminar I |
0 |
Fall 2014-2015 |
Graduate Seminar I |
0 |
Fall 2013-2014 |
Graduate Seminar I |
0 |
Fall 2012-2013 |
Graduate Seminar I |
0 |
Fall 2011-2012 |
Graduate Seminar I |
0 |
Fall 2010-2011 |
Graduate Seminar I |
0 |
Fall 2009-2010 |
Graduate Seminar I |
0 |
Fall 2008-2009 |
Graduate Seminar I |
0 |
Fall 2007-2008 |
Graduate Seminar I |
0 |
Fall 2006-2007 |
Graduate Seminar I |
0 |
Fall 2005-2006 |
Graduate Seminar I |
0 |
Fall 2004-2005 |
Graduate Seminar I |
0 |
Fall 2003-2004 |
Graduate Seminar I |
0 |
Fall 2001-2002 |
Graduate Seminar I |
0 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 1 ECTS (1 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 552 Graduate Seminar II |
0 Credit |
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2023-2024 |
Graduate Seminar II |
0 |
Fall 2023-2024 |
Graduate Seminar II |
0 |
Spring 2022-2023 |
Graduate Seminar II |
0 |
Fall 2022-2023 |
Graduate Seminar II |
0 |
Spring 2021-2022 |
Graduate Seminar II |
0 |
Fall 2021-2022 |
Graduate Seminar II |
0 |
Spring 2020-2021 |
Graduate Seminar II |
0 |
Fall 2020-2021 |
Graduate Seminar II |
0 |
Spring 2019-2020 |
Graduate Seminar II |
0 |
Fall 2019-2020 |
Graduate Seminar II |
0 |
Spring 2018-2019 |
Graduate Seminar II |
0 |
Fall 2018-2019 |
Graduate Seminar II |
0 |
Spring 2017-2018 |
Graduate Seminar II |
0 |
Spring 2015-2016 |
Graduate Seminar II |
0 |
Spring 2014-2015 |
Graduate Seminar II |
0 |
Spring 2013-2014 |
Graduate Seminar II |
0 |
Spring 2012-2013 |
Graduate Seminar II |
0 |
Spring 2011-2012 |
Graduate Seminar II |
0 |
Spring 2010-2011 |
Graduate Seminar II |
0 |
Spring 2009-2010 |
Graduate Seminar II |
0 |
Spring 2008-2009 |
Graduate Seminar II |
0 |
Spring 2007-2008 |
Graduate Seminar II |
0 |
Spring 2006-2007 |
Graduate Seminar II |
0 |
Spring 2005-2006 |
Graduate Seminar II |
0 |
Spring 2004-2005 |
Graduate Seminar II |
0 |
Spring 2003-2004 |
Graduate Seminar II |
0 |
Spring 2002-2003 |
Graduate Seminar II |
0 |
Spring 2001-2002 |
Graduate Seminar II |
0 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 1 ECTS (1 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 560 Automated Debugging |
3 Credits |
Program debugging is a process of identifying and
fixing bugs. Identifying root causes is the hardest,
thus the most expensive, component of debugging.
Developers often take a slice of the statements
involved in a failure, hypothesize a set of potential
causes in an ad hoc manner, and iteratively verify
and refine their hypotheses until root causes are
located. Obviously, this process can be quite tedious
and time-consuming. Furthermore, as software
systems are getting increasingly complex, the
inefficiencies of the manual debugging process are
getting magnified.
Many automated approaches have been proposed
to facilitate program debugging. All these
approaches share the same ultimate goal, which
is to help developers quickly and accurately pinpoint
the root causes of failures.
This course will cover state-of-the-art automated
debugging approaches from both practical and
research perspectives and will consist of two main
parts. The goal of the first part is two folds: 1) To
turn program debugging from a black art (as
many believe) into a systematic and well-organized
discipline; and 2) To provide students with enough
background information to read and understand
the scientific literature. The topics which will be
covered in the first part are: How Failures Come To
Be, Tracking Problems, Making Programs Fail,
Reproducing Problems, Simplifying Problems,
Scientific Debugging, Deducing Errors, and Mining
and Detecting Anomalies. The second part of the
course will survey the related literature by dividing
it into four broad categories, namely static-
analysis-based, dynamic-analysis-based,
model-based, and empirical approaches.
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2022-2023 |
Automated Debugging |
3 |
Fall 2021-2022 |
Automated Debugging |
3 |
Fall 2020-2021 |
Automated Debugging |
3 |
Fall 2019-2020 |
Automated Debugging |
3 |
Spring 2018-2019 |
Automated Debugging |
3 |
Spring 2017-2018 |
Automated Debugging |
3 |
Fall 2016-2017 |
Automated Debugging |
3 |
Fall 2015-2016 |
Automated Debugging |
3 |
Spring 2014-2015 |
Automated Debugging |
3 |
Fall 2013-2014 |
Automated Debugging |
3 |
Fall 2012-2013 |
Automated Debugging |
3 |
Fall 2011-2012 |
Automated Debugging |
3 |
Fall 2010-2011 |
Automated Debugging |
3 |
Fall 2009-2010 |
Automated Debugging |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 58004 Special Topics in CS: Graph Mining |
3 Credits |
This course focuses on advanced graph mining
algorithms for understanding graphs and extracting
patterns and relationships from them. The course covers
the following topics: Graph data structures and graph
databases, paths flows and fundamental graph
algorithms, mining subgraph patterns, subgraph pattern
matching, nearest-neighbors search, graph centrality,
spectral graph theory, graph similarity & graph kernels,
modularity & influence maximization, graph
embeddings & graph classification, linear-algebra-based
graph algorithms
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2020-2021 |
Special Topics in CS: Graph Mining |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 58007 Special Topics in CS: Internet of Things Sensing System |
3 Credits |
Introduction to the Internet of Things, Examples of mobile
and embedded systems, and sensors,
Sensing Pipelines, Signal Processing for sensor data,
Machine Learning for sensing, System Considerations,
Networking for IoT, Energy preservation, Privacy in
Sensing, Embedded Sensing Architectures, On-device
sensing on smartphones, Sensing with purpose-built devices
on the edge, Wearable devices, Edge/Cloud Computing in
Sensing, Remote inference, Offloading computations,
Prominent Applications
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2023-2024 |
Special Topics in CS: Internet of Things Sensing System |
3 |
Fall 2022-2023 |
Special Topics in CS: Internet of Things Sensing System |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 58008 Special Topics in CS: Automated Program Repair |
3 Credits |
This course is about introducing automatic software repair
and its fundamental concepts and, exploring the current
state-of-the-art in the field of automated program repair.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2022-2023 |
Special Topics in CS: Automated Program Repair |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 58009 Special Topics in CS: Lattice-Based Cryptography and Homomorphic Encryption Schemes |
3 Credits |
Mathematical Background, Partial Homomorphic
Encryption Schemes (Paillier and Damgard-Jurik
Encryption Schemes), Lattice-Based Cryptography,
Ring-LWE problem, the LLL Algorithm,
Homomorphic Encryption Schemes (BGV, BFV,
CKKS), Bootstrapping, Scheme Switching, Multi-key
Homomorphic Encryption, Applications of
Homomorphic Encryption on Machine Learning,
Acceleration of NTT Algorithm for Homomorphic Encryption
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2022-2023 |
Special Topics in CS: Lattice-Based Cryptography and Homomorphic Encryption Schemes |
3 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 590 Master Thesis |
0 Credit |
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2023-2024 |
Master Thesis |
0 |
Fall 2023-2024 |
Master Thesis |
0 |
Spring 2022-2023 |
Master Thesis |
0 |
Fall 2022-2023 |
Master Thesis |
0 |
Spring 2021-2022 |
Master Thesis |
0 |
Fall 2021-2022 |
Master Thesis |
0 |
Spring 2020-2021 |
Master Thesis |
0 |
Fall 2020-2021 |
Master Thesis |
0 |
Spring 2019-2020 |
Master Thesis |
0 |
Fall 2019-2020 |
Master Thesis |
0 |
Spring 2018-2019 |
Master Thesis |
0 |
Fall 2018-2019 |
Master Thesis |
0 |
Spring 2017-2018 |
Master Thesis |
0 |
Fall 2017-2018 |
Master Thesis |
0 |
Spring 2016-2017 |
Master Thesis |
0 |
Fall 2016-2017 |
Master Thesis |
0 |
Spring 2015-2016 |
Master Thesis |
0 |
Fall 2015-2016 |
Master Thesis |
0 |
Spring 2014-2015 |
Master Thesis |
0 |
Fall 2014-2015 |
Master Thesis |
0 |
Spring 2013-2014 |
Master Thesis |
0 |
Fall 2013-2014 |
Master Thesis |
0 |
Spring 2012-2013 |
Master Thesis |
0 |
Fall 2012-2013 |
Master Thesis |
0 |
Spring 2011-2012 |
Master Thesis |
0 |
Fall 2011-2012 |
Master Thesis |
0 |
Spring 2010-2011 |
Master Thesis |
0 |
Fall 2010-2011 |
Master Thesis |
0 |
Spring 2009-2010 |
Master Thesis |
0 |
Fall 2009-2010 |
Master Thesis |
0 |
Spring 2008-2009 |
Master Thesis |
0 |
Fall 2008-2009 |
Master Thesis |
0 |
Spring 2007-2008 |
Master Thesis |
0 |
Fall 2007-2008 |
Master Thesis |
0 |
Spring 2006-2007 |
Master Thesis |
0 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 50 ECTS (50 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 592 Project |
0 Credit |
All graduate students pursuing a non-thesis M.Sc. Program
are required to complete a project. The project topic and
contents are based on the interest and background of the
student and are approved by the faculty member serving as
the project supervisor. At the completion of the project,
the student is required to submit a final report. The final
report is to be approved by the project supervisor.
|
Last Offered Terms |
Course Name |
SU Credit |
Fall 2007-2008 |
Project |
0 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 20 ECTS (20 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|
CS 790 Ph.D.Dissertation |
0 Credit |
|
Last Offered Terms |
Course Name |
SU Credit |
Spring 2023-2024 |
Ph.D.Dissertation |
0 |
Fall 2023-2024 |
Ph.D.Dissertation |
0 |
Spring 2022-2023 |
Ph.D.Dissertation |
0 |
Fall 2022-2023 |
Ph.D.Dissertation |
0 |
Spring 2021-2022 |
Ph.D.Dissertation |
0 |
Fall 2021-2022 |
Ph.D.Dissertation |
0 |
Spring 2020-2021 |
Ph.D.Dissertation |
0 |
Fall 2020-2021 |
Ph.D.Dissertation |
0 |
Spring 2019-2020 |
Ph.D.Dissertation |
0 |
Fall 2019-2020 |
Ph.D.Dissertation |
0 |
Spring 2018-2019 |
Ph.D.Dissertation |
0 |
Fall 2018-2019 |
Ph.D.Dissertation |
0 |
Spring 2017-2018 |
Ph.D.Dissertation |
0 |
Fall 2017-2018 |
Ph.D.Dissertation |
0 |
Spring 2016-2017 |
Ph.D.Dissertation |
0 |
Fall 2016-2017 |
Ph.D.Dissertation |
0 |
Spring 2015-2016 |
Ph.D.Dissertation |
0 |
Fall 2015-2016 |
Ph.D.Dissertation |
0 |
Spring 2014-2015 |
Ph.D.Dissertation |
0 |
Fall 2014-2015 |
Ph.D.Dissertation |
0 |
Spring 2013-2014 |
Ph.D.Dissertation |
0 |
Fall 2013-2014 |
Ph.D.Dissertation |
0 |
Spring 2012-2013 |
Ph.D.Dissertation |
0 |
Fall 2012-2013 |
Ph.D.Dissertation |
0 |
Spring 2011-2012 |
Ph.D.Dissertation |
0 |
Fall 2011-2012 |
Ph.D.Dissertation |
0 |
Spring 2010-2011 |
Ph.D.Dissertation |
0 |
Fall 2010-2011 |
Ph.D.Dissertation |
0 |
Spring 2009-2010 |
Ph.D.Dissertation |
0 |
Fall 2009-2010 |
Ph.D.Dissertation |
0 |
Spring 2008-2009 |
Ph.D.Dissertation |
0 |
Fall 2008-2009 |
Ph.D.Dissertation |
0 |
Spring 2007-2008 |
Ph.D.Dissertation |
0 |
|
Prerequisite: __ |
Corequisite: __ |
ECTS Credit: 180 ECTS (180 ECTS for students admitted before 2013-14 Academic Year) |
General Requirements: |
|
|