Center of Excellence in Higher Education
The First Private University in Bangladesh

Program : MS in Computer Science & Engineering


The overall objective of Computer Science & Engineering (CSE) education is to develop human resources with the capability of solving problems related to the growth of the society with the help of computing technology. Over the years, North South University (NSU) has developed an internationally competitive undergraduate Computer Science & Engineering program, modeled after similar programs offered by leading North American Universities. Graduates of this program have demonstrated excellence in industry and graduate studies both at home and abroad.

Leveraging the success of undergraduate program, NSU envisions developing the graduate program with the focus on research and technopreneurship. The CSE graduate program has been designed with the objective of creating and deploying new computing knowledge for enhancing the quality of life of citizens of Bangladesh and the rest of the world. In the process of developing this program, NSU’s CSE department performed extensive investigation of the research report on computer science education produced by Association for Computing Machinery (ACM) and Institute of Electrical and Electronic Engineers’ (IEEE) Computer Society, reviewed similar graduate programs of leading North American Universities, studied the structure and dynamics of local and global IT industry and consulted with notable academicians and entrepreneurs in the area of Computer Science and Engineering.

North South University understands that it would be a formidable challenge for developing a world class CSE graduate program in Bangladesh. Over the years, NSU has invested significantly in developing the knowledge infrastructure comprising state-of-the-art library, computer laboratories and esteemed faculty members with international reputation. This basic infrastructure will work as strong foundation for the growth of this new graduate program to the standard of similar programs offered by leading North American Universities.

This graduate program is designed with the research focus on distributed multimedia computing, robotics and intelligent machines, software engineering, computer networking, algorithms and system complexity, computer graphics and visualization, and computer architecture, telecommunications and bioinformatics. A balanced approach has been taken in course and thesis works in order to develop both the breadth and depth of knowledge in graduating students.

Admission Requirements

General requirements for admission to the Masters in Computer Science program:

  1. a) A 4-year bachelor's or equivalent degree in Computer Science/Computer Engineering/Computer Science  and Engineering/Information Technology or any computer related areas from an accredited public or private university in Bangladesh or abroad with a grade point average of at least 3.0 (in the 4.0 scale). Students having a CGPA of less than 3.0 may be considered if the GRE Computer Science subject test score is submitted.

      b)A 3-year bachelor's or equivalent degree in Computer Science/Computer
      Engineering/Computer Science and Engineering/Information Technology or any
      computer related areas from an accredited public or private university in Bangladesh
      or abroad with a grade point average of at least 3.0 (in the 4.0 scale) may be
      admitted on condition that at least 15 credits of foundation courses from the
      undergraduate curriculum must be completed at NSU in order to be a regular student
      in the MS in Computer Science & Engineering program.

  2. Acceptable score in the NSU Administered Admission Test or a score of 1100 in the
     Quantitative and Verbal part of the Graduate Record Examination (GRE) General Test.

  3. Three letters of recommendation, at least two must be from the faculty members of
     the last institution attended.

Degree Requirements

The general requirements for the Masters’ in Computer Science & Engineering degree are as follows:

The general requirements for the Masters’ in Computer Science & Engineering degree are as follows:

  1. A Thesis with 24 credits course work and passing of comprehensive Examinations in 3 areas (30 credits)
  2. A Project with 27 credits course work and passing of comprehensive Examinations in 4 areas (30 credits)
  3. Two special courses (6 credits=3+3) with 27 credit course work and passing of comprehensive Examinations in 5 areas - (33 credits)

MS-CSE students will sit for comprehensive examination/ qualifying exmination to test their strength in undergraduate (CSE background) program.

The minimum and maximum time to complete the degree requirements are 3 semesters and 5 years respectively from the initial enrolment of the Masters’ program. The residency requirement is 21 credit hours including the Masters’ Thesis. A maximum of 9 credits are transferable from other universities. 

“A student must complete required number of credits with minimum CGPA of 3.0 on a 4 point scale to earn the degree. To continue in the program a student must maintain a minimum CGPA of 3.0 at all levels of academic advancement. If in any semester the CGPA drops below 2.70 the student will be on academic probation. If a student remains on probation for two consecutive semesters, he/she will be dismissed from the program


Course Requirement:

To fulfill the degree requirement, students in the Computer Science & Engineering Masters’ program will have to take eight courses, covering at least four of the following six groups. Students completing two courses and a thesis in one of these groups will receive the degree with concentration in that area.

Group 1: Algorithms




Advanced Algorithms


Computational Complexity


Parallel Algorithms


Formal Language and Automata Theory


Graph Theory




Group 2: Computer Networks & Systems




Distributed Database Systems


Distributed Operating Systems


Advanced Computer Architecture


Advanced VLSI Design


Advanced Computer Networks


Modeling and Simulation


Multimedia Data Technologies


Group 3: Intelligent System Engineering




Advanced Artificial Intelligence


Advanced Neural Networks


Machine Learning


Introduction to Robotics


Computer Vision


Genetic Algorithm


Theory of Fuzzy Systems


Digital Image Processing


Group 4: Software Engineering




Programming Language Design


Formal Methods in Software Engineering


Software Quality Assurance


Intellectual Property and Contract Law


Economic Issues in Computing


Advanced Topics on Management of Technology


Group 5: Telecommunication Engineering




Fiber-Optic Communications System II


Network Operations and Management


Telecommunications Systems Engineering


Mobile & Wireless Communication System


Telecommunications Business and Management


Digital Signal Processing


Group 6: Bioinformatics




Bioinformatics Computing


Molecular Biology


Micro array Bioinformatics


Genome Sequencing & Analysis


Structural Bioinformatics


Special Courses

CSE 590: Project

CSE 596: Special Course I: Recent advances in CSE

CSE 597: Special Course 2: Seminar Topics

CSE 598: Special Topic

CSE 599: Thesis

Qualifying Examination:

There will be a qualifying exam administered at the beginning of each semester in the following 10 areas:

1. Algorithm Analysis                                     2. Operating Systems

3. Compiler Construction                                4. Computer Network

5. Database Systems                                       6. Artificial Intelligence

7. Computer Architecture                               8. Software Engineering

9. Programming Languages Principles            10.Formal Languages and Automata Theory

All students are required to pass the qualifying examination in 4 areas within the 3 semesters of their enrolment. The students can sit for the examinations at most two times to pass in their chosen 4 areas. They will be allowed to sit in only for the required number of areas. (For example, a student needing to pass in 2 areas in his/her 2nd attempt may not appear in 3 or more areas). Failure to pass the qualifying examination will result in the discontinuation from the Masters program. To avoid being dismissed from the program students are strongly advised to take relevant undergraduate courses before making the 2nd attempt.

Thesis Committee:

After passing the qualifying examinations, a Thesis Committee will be formed for the student. The supervisor, in consultation with the Graduate Adviser, will form a four-member Thesis Committee that should include one external member.

Thesis Committee advises the student regarding the direction of the course works and suggests possible research areas. The Committee would evaluate the student’s progress time to time and may suggest a few presentations in a seminar prior to the final thesis defense. The Thesis supervisor determines whether to enroll CSE599 as a one-semester 6-credit course or a two-semester having 3-credits each.

Course Description

MS-CSE Core and Elective Engineering Courses

CSE 511: Advanced Algorithms

Principles underlying the design and analysis of efficient algorithms. Topics to be covered include: divide-and-conquer algorithms, graph algorithms, matroids and greedy algorithms, randomized algorithms, NP-completeness, approximation algorithms, linear programming. 3 credits.

CSE 512: Distributed Database Systems

In this course, students will learn different distributed database management algorithms to support concurrency, transaction management, query optimization, replication, recovery, distributed database design and security; implement a client-server DBMS and distributed database applications. Distributed databases - various contemporary issues including data model partitioning, fragmentation, replication issues, query optimization, concurrency control, restart and recovery, distributed database design, client-server and distributed database applications will be discussed in details. Students will build a distributed system with Oracle DBMS. Particular attention will be paid to detailed consideration of distributed database management issues. 3 credits.

CSE 521: Computational Complexity

An in-depth introduction to the main models and concepts of the mathematical theory of computation, including: Computability: What problems can be solved in principle? How might you prove that a problem can't be solved? Complexity: What problems can be solved within given resource constraints? How do constraints on different resources (e.g., time, space, or parallel time) relate? Logic: What are the best ways to formally specify a problem, and how do these specifications relate to the difficulty of the problem? 3 credits.

CSE 531: Parallel Algorithms

The course will primarily cover the design and analysis of parallel algorithms,  computational models and complexity classes. Parallel algorithms for various problems including: basic arithmetic, sorting, searching, selection, graph theory, matrix computations, combinatorial enumeration, optimization, computational geometry, and numerical analysis will also be extensively studied in the course. 3 credits

CSE 541: Formal Language and Automata Theory

This course will give an introduction to formal languages and automata theory. Automata and formal languages appear (possibly in various disguises) in almost every branch of computer science. A formal language is a set of strings where a string is a finite sequence of symbols. An example of a formal language is the set of all ``syntactically correct'' Pascal programs (accepted by a certain compiler). A main problem that will be discussed is how to define an infinite language in a finite way. A related problem is to construct an algorithm that can decide whether a string is in the language or not. Both problems are of practical importance, for instance for constructing compilers and design of programming languages. At the end of the course, students will be introduced to the theory of computability. 3 Credits.

CSE 551: Graph Theory

This course starts with the basics of graphs, digraphs, and networks. It covers spanning trees, connectivity, traversal, planarity, coloring, network flows, algebraic specification of networks, and layouts on surfaces. Drawings and concrete examples abound. Applications concentrate on graphs as models for computer science, operations research, and sociology, including special attention to software design and to parallel architectures. 3 credits

CSE 561: Cryptography

Origins, computer arithmetic and complexity- what is cryptography, a history of factoring and primality testing, computer arithmetic and complexity, Symmetric-key cryptosystems- an introduction to congruences, block ciphers, DES cryptanalysis, successor AES, stream ciphers, Public-key crypto-systems- exponentiation, discrete logs, public key cryptography, authentication, knapsacks, Primality Testing- an introduction to primitive roots, true primality tests, probabilistic primality tests, Agrawal algorithm,

Factoring- three algorithms, the number field sieve, Advanced topics – elliptic curves and cryptography, zero knowledge, quantum cryptography. 3 credits. 

CSE 522: Distributed Operating Systems:

This course provides an in-depth examination of principles of distributed operating systems. Covered topics include processes and threads, concurrent programming, distributed interprocess communication, distributed process scheduling, shared virtual memory, distributed file systems. In-depth examples will be taken from current operating systems such as UNIX and MACH. Some coverage of operating system principles for multiprocessors will also be included. 3 credits. 

CSE 532: Advanced Computer Architecture

This course examines the structure of modern computer systems. We explore hardware and technology trends that have led to current machine organizations, then consider specific features and their impact on software and performance. These may include superscalar issue, caches, pipelines, branch prediction, and parallelism. Midterm and final exams, team project, homework, in-class exercises. 3 credits.

CSE 542: Advanced VLSI Design

Review of CMOS logic circuits; impact of fabrication issues on design; high speed switching circuits; high performance memory structures; advanced clocking strategies and clock distribution; performance optimization; deep sub-micron design issues; ASIC design flow: logic synthesis, placement and routing; design verification; low power design.  Students will learn and participate in the process of design, simulation and layout of a complex digital system. 3 credits.

CSE 552: Advanced Computer Networks

Computer Networks is a graduate course that introduces fundamental concepts in the design and implementation of computer communication networks and their protocols. Topics include: layered network architectures, applications, transport and routing, IP version 6, mobile IP, multicasting, session initiation protocol, quality of service, network security, network management, and TCP/IP in wireless networks. An emphasis will be placed on the protocols used in the Internet. 3 credits

CSE 562: Modeling and Simulation

Probability, random variables and their properties, mathematical expectation, specific discrete and continuous random variates (Poisson, exponential, etc.). Simulation tools, random number and variate generation, event serialisation and time advance algorithms; process and resource classes, Performance measures, model instrumentation and result presentation. Simple stochastic processes - discrete time Markov chains, continuous time Markov processes; Poisson process, Birth and Death process and their application to the simple (e.g. M/M/1) queues. More advanced queuing theory - multi-server queues, non-Markovian queues, networks of queues. mean value analysis (analytic derivation of throughput, utilisation, mean queue size and delay). Applications - case studies in computer systems and networks using analysis and simulation, advanced simulation software. 3 credits.

CSE 572: Multimedia Data Technologies

This course will review recent developments in transparent data embedding and watermarking for audio, image, and video. Data-embedding and watermarking algorithms embed text, binary streams, audio, image, or video in a host audio, image, or video signal. The embedded data are perceptually inaudible or invisible to maintain the quality of the source data. The embedded data can add features to the host multimedia signal, e.g., multilingual soundtracks in a movie, or provide copyright protection. The course will also discuss the reliability of data-embedding procedures and their ability to deliver new services such as viewing a movie in a given rated version from a single multicast stream. Other topics will cover issues and problems associated with copy and copyright protections and assess the viability of current watermarking algorithms as a means for protecting copyrighted data. 3 credits.

CSE 513: Advanced Artificial Intelligence

In-depth introduction to Artificial Intelligence focusing on techniques that allow intelligent systems to operate in real-time and cope with missing information, uncertainty, and limited computational resources. Topics include: advanced search and problem-solving techniques, resource-bounded search, principles of knowledge representation and reasoning, meta-reasoning, reasoning under uncertainty, Bayesian networks and influence diagrams, decision theory and the value of information, planning and scheduling, intelligent agents architectures, and learning. 3 credits.

CSE 523: Advanced Neural Networks

This is a research course tailored to the needs of the current students enrolled, there is no strictly set syllabus. Following is a list of the topics which will be covered in depth in this course: Extended ASOCS coverage, Hamming Networks, Learning by a critic and the Associate Search Network, Genetic Algorithms, Self Organizing Topological Feature Maps, Counterpropagation networks, BAM's (bidirectional associative memories), Boolean Networks, RCE's (Restricted Coloumb Energy Networks), Implementation of actual applications in neural networks. 3 credits.

CSE 533: Machine Learning

This course covers a variety of methods that enable a machine to learn. We will cover as much of Duda, Hart, & Stork's ÔPattern RecognitionÕ as time permits. Topics will include Bayesian decision theory, maximum-likelihood estimation, expectation maximization, nearest-neighbor methods, linear discriminants, support vector machines, artificial neural networks, classification and regression trees, ensemble classifiers, clustering, and self-organizing feature maps. There will be weekly problem sets including some programming. There will be a midterm, and a final exam. 3 credits.

CSE 543: Introduction to Robotics

In addition to traditions rooted in mechanics and dynamics, geometrical reasoning, and artificial intelligence, the study of robot systems is growing to include many issues traditionally part of the computing sciences; distributed and adaptive control, architecture, software engineering, real-time systems, information processing and learning. In robotics, processing and its relationship to mechanical function are dependent on the target platform and the world in which it is situated. Designing an embedded computational system for sensory and motor processes requires that designers appreciate and understand all of these disciplines. This course is concerned with the design and analysis of adaptive, closed-loop physical systems. The focus will be sensory and motor systems that interpret and manipulate their environments. Toward this end, we will study mechanisms (kinematics and dynamics), actuators, sensors (with a focus on active vision), signal processing, associative memory, feedback control theory, supervised and unsupervised learning, and task planning. Interesting examples of integrated sensory, motor, and computational systems can be found in nature, so occasionally we will relate the subject matter to biological systems. Students will experiment with system identification and control, image processing, path planning, and learning on simulated platforms to reinforce the material presented in class. 3 credits

CSE 553: Computer Vision

People are able to infer the characteristics of a scene or object from an image of it. In this course, we will study what is involved in building artificial systems which try to infer such characteristics from an image. Topics include: Basics of image formation - the effect of geometry, viewpoint, lighting and albedo on image formation. Basic image operations such as filtering, convolution and correlation. Frequency representations of images. The importance of scale in images. Measurements of image properties such as color, texture, appearance and shape. Inference of motion and structure from moving objects and images. Detecting and recognizing objects in images. 3 credits.

CSE 563: Genetic Algorithm

Introductory material to Genetic Algorithm, Classical and Steady state Genetic Agorithm, Genetic operators, schemata and Genetic algorithm, Genetic programming, Evolution Strategies and Evolution Programming, Learning Classifiers System, Multimodal function optimization, Multi-objective problems, combinatorial optimization problems, Biology & Chemistry Applications. 3 credits.

CSE 573: Theory of Fuzzy Systems

Introduction to Neuro-Fuzzy and Soft Computing, Soft Computing and AI, Neural Networks, Fuzzy Set Theory, MF Formulation and Parameterization, Fuzzy Union, Intersection, and Complement, Fuzzy Rules and Fuzzy Reasoning, Fuzzy Inference Systems, Regression and Optimization, Supervised Learning Neural Networks, Neuro-Fuzzy Modeling, ANFIS, Neuro-Fuzzy Control, ANFIS Applications. 3 credits.

CSE 583 Digital Image Processing. Introduction; Point operations; Histograms; Spatial operations; Affine transformations; Image rectification; Interpolation and other transformations; Contrast enhancement; Convolution operation, Magnification and Zooming; Fourier transform; Edge detection; Boundary extraction and representation; Mathematical morphology. 3 credits.

CSE 514: Programming Language Design

This course uses a detailed examination of the Java and ML programming languages as a basis for studying fundamental principles underlying the design and implementation of modern programming languages. The course addresses a wide range of programming language concepts and issues from both a practical and a theoretical perspective. Some attention is given to such traditional topics as control constructs, type systems and type checking, since these are the foundations for all subsequent developments. The bulk of the course, however, is devoted to more contemporary language features such as object orientation, modularity, polymorphism and concurrency. Our study of both traditional topics and contemporary features is driven by first exploring their realization in Java and ML, then comparing and contrasting with the realizations found in other modern languages such as C++, C#, Ada 95 and Modula-3. In addition, we consider some emerging concepts and directions for programming languages such as orthogonal persistence, reflection, interoperability and open implementation. While the predominant paradigm for contemporary programming languages -- the imperative, object-oriented paradigm -- is our primary focus, and the functional paradigm is our secondary focus, other paradigms such as the logic programming paradigm are also discussed. Homework problems, programming exercises and projects reinforce the material covered in lectures and readings. 3 credits

CSE 524: Formal Methods in Software Engineering

This course introduces students to the principal activities involved in developing high-quality software systems. The course stresses the use of defined, systematic processes in the creation of carefully defined and engineered software products. Among the topics covered are requirements analysis, software architecture, formal specification methods, process definition, software design methods, and test planning. Issues specific to the development of software by teams and groups will also be addressed. Students will be required to read selected papers from the literature and complete homework projects. 3 credits.

CSE 534: Software Quality Assurance

This course will survey current research in developing tools and techniques for assuring software quality. As computing technology continues to permeate every aspect of personal and public life, the need for assuring the reliability of our computing infrastructure is increasing steadily. Driven by these societal needs, software quality research has become very active in the last few years. This course will survey current work in this area. While research in software engineering is as old as programming, recent approaches have broken new ground and there is currently a great deal of ferment. Thus, this course will necessarily take in a broad selection of topics, including research in testing, monitoring of running systems, capturing and querying program traces, several variations on extending type systems and other static analyses, theorem proving systems, and software model checking. The syllabus will be entirely research papers. 3 credits.

CSE 544: Intellectual Property and Contract Law

This course exposes students to issues of professional practice, ethical behaviour, and computer law. Topics included may be history of computing, impact of computer on the society, computing careers, legal and ethical responsibilities, intellectual property rights management, copyrights, patents law, trade secrets, software piracy, laws related to information security and ethical responsibilities of computing profession. 3 credits.

CSE 554: Economic Issues in Computing

The objective of this course is to summarize the rational for antimonopoly efforts, describe several ways in which the information technology industry is affected by shortages of labor supply, suggest and defend ways to address limitations on access to computing, and outline the evolution pf pricing strategies for computing goods and services. Suggested topics to be covered are monopolies and their economic implications, effect of skilled labor supply and demand on the quality of computing products, pricing strategies in the computing domain, and differences in access to computing resources and the possible effects thereof. 3 credits.

CSE 564: Advanced Topics on Management of Technology

This course provides an in-depth understanding about the management of technology and innovation. Covered topics are: management of dynamic changes in R&D, integration of technology planning, product planning, business planning and the market demands, human, social and environmental concerns associated with technological change, case studies of global technology firms, business case development for deployment of new technologies critical decision analysis methods, new challenges and responses in technology management, technology transfer, and business and technology strategy. 3 Credits.

CSE 515: Fiber-Optic Communications System II

Telecommunications: Point-to-Point Systems and Networks, Information Carrying Capacity, The Need for Fiber-Optic Communications Systems, A Fiber-Optic Communications System: The Basic Blocks, Worldwide Submarine Networks, Electromagnetic Spectrum, Radiation & Absorption, Optical Fibers-Basics: Step-Index Fiber, Numerical Aperture, Attenuation, Calculation of Total Attenuation, Intermodal and Chromatic Dispersion, Graded-Index Fiber, The Structure of a Singlemode Fiber, Bit Rate and Bandwidth, Cutoff Wavelength, V-Number, Attenuation Constant, Dispersion in Multimode Fibers, Electrical & Optical Bandwidth, Spectral Width, Singlemode Fibers, Gaussian Beam, Bit Rate of a Singlemode Fiber, Fabrication, Cabling, and Installation, Fiber Cable Connectorization and Testing, Power Budget, Light Sources, Transmitters and Receivers; Transmitter Modules, Receiver Units, Components of Fiber-Optic Networks; Passive Components, Switches, Transceivers, TDM,  WDM and DWDM systems, Add/Drop Problem, Multiplexing Hierarchy in Telecommunications, SONET & SDH Systems, FDDI, and Functional Modules of Fiber-Optic Networks-Telephone and Computer Networks, Networks, Protocols, and Services, OSI, ATM Networks and Layers; Broadband Communication System, Network Management and Future of Fiber-Optic Networks. 3 credits.  

CSE 525: Network Operations and Management

Microcomputer principles and Applications. Fundamental characteristics of the software life cycle. Tools. techniques and management controls for development and maintenance of large software systems. Software metrics and models. Human factors and experimental design. 3 credits.

CSE 535: Telecommunications Systems Engineering

Technical survey of the ways and means that voice, data and video traffic are moved long distance. Topics covered include Data Networks (Ethernet and Token Ring Local Area Networks; FDDI and SMDS Metropolitan Networks; Internet, Frame Relay, and ATM Wide Area Networks); The Telephone System (POTs, Network Synchronization and switching, ISDN, SONET, cellular Telephone); and video (NTSC, Switching and Timing, compressed Video standards such as MPEG and Px64, HDTV). 3 credits.

CSE 545: Mobile & Wireless Communication System

Aspects of radiowave propagation for fixed and mobile communication systems, and cellular system design. Large-scale and small-scale propagation models, multipath fading, link-budget, interference and frequency reuse, multiple access schemes and system capacity. Trunking and grade of service, wireless network planning and operation. Architecture and operation of 2G cellular mobile systems, 2.5 G and 3G technologies. Special techniques/Diversity, Equalization, Interleaving, and Smart Antenna. 3 credits.

CSE 555: Telecommunications Business and Management

Telecom services, local, long distance, mobile telephony, voice over IP, business models, operations and maintenance, cost and pricing for service packages, wireless vs wireline telephony, industry dynamics, market competition, regulatory issues, cost of compliance, ITU and telecom policy for local nd global market, industry restructure, privitazation trends in developing markets, spectrum management, licensing and fees, tariff, interconnection, overseas access, business models. 3 credits.

CSE 565: Digital Signal Processing

The purpose of this course is to give the students of Computer Science/Engineering the basic background in Digital Signal Processing. This course introduces how a computer (a general purpose or special purpose DSP chip) could be used to solve Signal Processing problems digitally. The topics include introduction to discrete signal and systems, difference equations, discrete convolution, Z-transform and Fast Fourier transform techniques. 3 credits.

CSE 516: Bioinformatics Computing

Retrieval and analysis of electronic information are essential in today's research environment. This course explores the theory and practice of biological database searching and analysis. In particular, students are introduced to integrated systems where a variety of data sources are connected through worldwide web access. Information retrieval as well as interpretation are discussed and many practical examples in a computer laboratory setting enable students to improve their data mining skills. Methods included in the course are searching the biomedical literature, sequence homology searching and multiple alignments, protein sequence motif analysis, and several genome analytical methods. 3 credits.

CSE 526: Molecular Biology

Topics from Genetics: DNA-Structure, function and replication, the polymerase chain reaction, sequencing, RNA-mechanism of synthesis and regulation, organization of eukaryotic/prokaryotic genomes, analyzing complex genomes and mapping, expression/differentiation/regulation of eukaryotic genes, principles of cloning, restriction enzymes, cloning vectors. Topics from Protein: Amino acids, protein structure, protein function (binding and catalysis), function and catalysis, polysaccharides, lipids, membranes, cell biology. 3 credits.

CSE 536: Microarray Bioinformatics

Uses of microarrays, sequence databases for microarrays, computer design of oligonucleotide probes, image processing, normalization, measuring and quantifying microarray variability, analysis of differentially expressed genes, analysis of relationships between genes, tissues or treatments, classification of tissues and samples, experimental design, data standards, storage and sharing. 3 credits.

CSE 546: Genome Sequencing and Analysis

The completion of the human genome sequence is just the latest achievement in genome sequencing. Armed with the complete genome sequence, scientists need to identify the genes encoded within, to assign functions to the genes, and to put these into functional and metabolic pathways. This course will provide an overview of the laboratory and computational techniques beginning with genome sequencing and annotation, extending to bioinformatics analysis and comparative genomics and including functional genomics. 3 credits.

CSE 556: Structural Bioinformatics

Deriving and visualizing macromolecular structures, X-ray crystallography, NMR spectroscopy, electron microscopy, structure visualization, examining and interpreting structural data, Protein Data Bank (PDB), structures classification schemes (CATH, SCOP), structure validation software, secondary structure assignment, structure comparison and alignment, domain assignment, functional assignment from structure, protein docking, comparative assessment of protein structure prediction (CASP), comparative modeling, fold recognition, ab-initio structure prediction. 3 credits.

CSC 598 Special Topic. A course on any contemporary topic in the field of Computer Science & Engineering. 3 credits.

CSC 599 Thesis

The thesis work will make original research contribution in the concentration research topics. It is expected to make the original that the thesis work will produce a reputable International Journals 6 credits.


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