Computer Science and Engineering
MS with Thesis
The MS program in Computer Sciences and Engineering aims to provide advanced education and a cutting edge research experience in computer engineering. The focus of the program is excellence in research. Graduates of the program can join industry or continue their research careers in PhD in Computer Sciences and Engineering programs.
Degree Requirements
Computer Science and Engineering master with thesis program consists of at least 7 courses of at least 21 credits and seminar courses and thesis studies.
Students are required to take at least seven (7) elective courses according to their interests and complete at least twenty-one (21) credits and seminar courses by the end of the 4th semester at the latest. Unless stated otherwise, courses are 3 credits. With the approval of their advisors, students can take a maximum of 2 undergraduate courses to provide a basis for graduate courses, and these undergraduate courses can be used to complete the course and credit requirements of the master with thesis program.
In addition to the credit courses, students must complete the non-credit courses; COMP 595 - Master Thesis, COMP 590 - Seminar, ETHR 500 - Scientific Research Methods and Research and Publication Ethics, ENGL 500 - Academic Writing, TEAC 500 - Teaching Experience, KOLT 500 -Teaching in Higher Education and LIBR 500 - Library Researcher Development.
Curriculum
Mandatory, non-credit courses:
COMP 590 SEMINAR
COMP 595 M.SC. THESIS
COMP 695 PH.D. THESIS
ENGL 500 GRADUATE WRITING
TEAC 500 TEACHING EXPERIENCE
LIBR 500 LIBRARY RESEARCHER DEVELOPMENT
M.Sc. and Ph.D. level elective courses:
COMP REGULAR OFFERINGS: 16 courses
COMP 504 DIGITAL SPEECH AND AUDIO PROCESSING
COMP 506 DIGITAL IMAGE AND VIDEO PROCESSING
COMP 508 COMPUTER VISION AND PATTERN RECOGNITION
COMP 510 COMPUTER GRAPHICS
COMP 513 INFORMATION THEORY
COMP 515 DISTRIBUTED COMPUTING SYSTEMS
COMP 529 PARALLEL PROGRAMMING
COMP 534 COMPUTER AND NETWORK SECURITY
COMP 537 INTELLIGENT USER INTERFACES
COMP 540 INFORMATION RETRIEVAL
COMP 541 MACHINE LEARNING
COMP 542 NATURAL LANGUAGE PROCESSING
COMP 543 MODERN CRYPTOGRAPHY
COMP 546 ALGORITHM DESIGN AND ANALYSIS
COMP 570 BIOINFORMATICS AND ALGORITHMS
COMP 589 SOFTWARE RELIABILITY: SPECIFICATION, TESTING AND VERIFICATION
COMP SPECIAL TOPICS or IRREGULAR OFFERINGS: 11 courses
COMP 544 COMPUTATION AND COMPLEXITY
COMP 550 ACTIVE LEARNING
COMP 550 ADVANCED CRYPTOGRAPHY
COMP 550 ADVANCES IN DISTRIBUTED SYSTEMS
COMP 550 ALTERNATIVE USER INTERFACES
COMP 550 BAYESIAN STATISTICS AND MACHINE LEARNING
COMP 550 COMPILER DESIGN
COMP 550 COMPUTATIONAL NATURAL LANGUAGE LEARNING
COMP 550 MONTE CARLO METHODS
COMP 550 SPEECH RECOGNITION
COMP 550 PARALLEL ARCHITECTURES
OTHER DEPARTMENTS: 32 courses
CMSE 501 INTRODUCTION TO COMPUTATIONAL SCIENCE
ELEC 501 RANDOM PROCESSES
ELEC 505 LINEAR SYSTEM THEORY
ELEC 511 DIGITAL COMMUNICATIONS
ELEC 514 WIRELESS COMMUNICATIONS
ELEC 528 WIRELESS NETWORKS
ELEC 530 DETECTION AND ESTIMATION THEORY
INDR 501 OPTIMIZATION MODELS AND ALGORITHMS
INDR 503 STOCHASTIC MODELS AND THEIR APPLICATIONS
INDR 511 ADVANCED OPTIMIZATION METHODS
INDR 513 ADVANCED STOCHASTIC PROCESSES
INDR 520 NETWORK MODELS AND OPTIMIZATION
INDR 530 DECISION ANALYSIS
INDR 562 INTEGER AND COMBINATORIAL OPTIMIZATION
INDR 564 DYNAMIC PROGRAMMING
INDR 566 SCHEDULING
INDR 568 HEURISTIC METHODS
INDR 574 STOCHASTIC MODELS IN FINANCIAL ENGINEERING
MATH 503 APPLIED MATHEMATICS I
MATH 504 NUMERICAL METHODS I
MATH 506 NUMERICAL METHODS II
MATH 521 ALGEBRA I
MATH 522 ALGEBRA II
MATH 525 ALGEBRATIC NUMBER THEORY
MATH 527 NUMBER THEORY
MATH 531 REAL ANALYSIS I
MATH 532 REAL ANALYSIS II
MATH 536 APPLIED FUNCTIONAL ANALYSIS I
MATH 565 GRAPH THEORY
MECH 522 COMPUTATIONAL FLUID DYNAMICS
MECH 534 COMPUTER BASED SIMULATION AND MODELING
MECH 544 ROBOTICS
Further elective courses may be taken with the advisor’s approval.
