Syllabus for M Sc

M.Sc. Syllabus, Session: 20010-2011

UNIVERSITY OF RAJSHAHI

Faculty of Science

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

(North Block, 4th Science Building)

Tel: 0721-711103

Fax: 0721-750064

E-mail:

Web Site: http://www.ru.ac.bd/cse

Syllabus for M.Sc.

Session: 2013–2014

EXAMINATION: 2014

University of Rajshahi

Faculty of Science

Department of Computer Science and Engineering

Syllabus for M.Sc. Degree

Session: 2013 - 2014

M.Sc. Examination: 2014

The Master of Science (M.Sc.) Courses in Computer Science and Engineering (CSE) are of one academic year and is not more than three academic years from the date of first admission. A student will study of 40 Credits with total 1000 Marks. The courses have been designed for two groups: (i) General and (ii) Thesis. The courses for the groups are distributed as follows:

(i) Courses for General Group:

Course Code / Course Title / Marks / Credits
CSE 501
CSE 502
CSE 503
CSE 504
CSE 505
Option I (T) / Pattern Recognition
Network Design and Management
Data Mining
Embedded Systems
Advanced Web Engineering
(One course should be selected from Table-I) / 100
100
100
100
100
100 / 4
4
4
4
4
4
CSE 514GT / Tutorial, Attendance and Continuous assessment / 100 / 4
CSE 515GV / General Viva Voce / 100 / 4
CSE 516P
(Marks:150 Credits:4) / CSE 516P (A): Pattern Recognition Lab.
CSE 516P (B): Network Design and Management Lab.
CSE 516P (C): Data Mining Lab.
CSE 516P (D): Embedded Systems Lab.
CSE 516P (E): Advanced Web Engineering Lab.
Option I (P): Lab related with option I (T) / 25
25
25
25
25
25 / 1
1
1
1
1
1
CSE 517J / Project / 50 / 2
Grand Total / 1000 / 40

(ii) Courses for Thesis Group:

Table I: Option I

Courses Code / Course Title / Marks / Credits
CSE 506
CSE 516P(F) / Human Computer Interaction
Human Computer Interaction Lab / 100
25 / 4
1
CSE 507
CSE 516P(G) / Computer Animation and Virtual Reality
Computer Animation and Virtual Reality Lab. / 100
25 / 4
1
CSE 508
CSE 516P(H) / Robotics and Intelligent Systems
Robotics and Intelligent Systems Lab. / 100
25 / 4
1
CSE 509
CSE 516P(I) / Mobile Communication
Mobile Communication Lab. / 100
25 / 4
1
CSE 510
CSE 516P(J) / Computer Vision
Computer Vision Lab. / 100
25 / 4
1
CSE 511
CSE 516P(K) / Mathematical Programming
Mathematical Programming Lab. / 100
25 / 4
1
CSE 512
CSE 516P(L) / Cloud Computing
Cloud Computing / 100
25 / 4
1
CSE 513
CSE 516P(M) / Natural Language Processing
Natural Language Processing / 100
25 / 4
1

*Tutorial 50% + Attendance 20% + Continuous assessment 30% =100%. Continuous assessment includes project and thesis progress presentation.

*The marks for attendance shall be awarded on the basis of attendance in the classes according to the following table:

Attendance / Marks / Attendance / Marks / Attendance / Marks
95-100% / 20% / 90-<95% / 18% / 85-<90% / 16%
80-<85% / 14% / 75-<80% / 12% / 70-<75% / 10%
65-<70% / 8% / 60-<65% / 6% / <60% / 0%

Brief descriptions of the Ordinance for the Master of Science (M.Sc.)

Degree, Faculty of Science, University of Rajshahi

Duration of the Course:

The M.Sc course consisting of General and Thesis Groups shall extend over a period of one academic year. The degree has to be completed within a minimum of one academic year and in not more than three academic years from the date of first admission.

Admission Requirements:

For admission to the M.Sc. course in CSE Department a student must have the following qualifications:

The Bachelor of Science with Honours Degree of four years duration of this University or of a recognised University in the CSE or similar subject. A maximum of two years’ break of study after passing B.Sc. Honours Examination shall be allowed.

Candidates appearing at the Bachelor of Science (B.Sc.) Honours final examination from this university may be admitted provisionally to the Master of Science (M.Sc.) classes pending publication of their examination results: the confirmation of their admission being subject to their passing the examination as and when the results of examination are published.

The number of seats in CSE Department will be determined by the CSE Academic Committee based on facilities available in the Department. Admission will be on the basis of merits (and if necessary), through admission test to be decided by the CSE Department.

Eligibility for examination:

In order to be eligible for taking the M.Sc. Examination, a candidate must have pursued a regular course of study by attending not less than 75% of the total number of classes held (theoretical, practical, tutorials etc.) provided that the Academic Committee of the CSE Department on special grounds and on such documentary evidence, as may be necessary, may recommend to the Vice-Chancellor cases of shortage of attendance ordinarily not below 60% for condonation. A candidate appearing in the examination under the benefit of this provision shall have to pay in addition to the examination fees, the requisite fee prescribed by the Syndicate for the purpose.

A candidate, who failed to appear at the examination or fails to pass the examination, may on the approval of the relevant Department be readmitted to the following session.

Admission to M.Sc Examination:

Every candidate for admission to M.Sc. examination shall submit his/her application in the prescribed from together with certificates of attendance and fulfill all other conditions prescribed by the University. The application shall be submitted through the chairman of the Department and Provost of the Hall be submitted through the Controller of Examinations at least six weeks before the date fixed for the commencement of the examination.

Medium of Questions and Answers: Questions shall be made in English. The medium of answer in the examination of all courses shall be in English.

The Grading Systems:

(a) Credit Point (CP): The credit points achieved by an examinee for 1 (one) unit course shall be 4(four).

Numerical Grade / LG / GP / CP/Unit
80% or its above / A+ (A Plus) / 4.00 / 4
75% to less than 80% / A (A Regular) / 3.75 / 4
70% to less than 75% / A- (A Minus) / 3.50 / 4
65% to less than 70% / B+ (B Plus) / 3.25 / 4
60% to less than 65% / B (B Regular) / 3.00 / 4
55% to less than 60% / B- (B Minus) / 2.75 / 4
50% to less than 55% / C+ (C Plus) / 2.50 / 4
45% to less than 50% / C (C Regular) / 2.25 / 4
40% to less than 45% / D / 2.00 / 4
Less than 40% / F / 0.00 / 0
Incomplete / I / -- / 0

(b) Letter Grade (LG) and Grade Point(GP): Letter Grades, corresponding Grade Points and Credit Points shall be awarded in accordance with provisions shown below:

Table of LG, GP and CP for credit courses

Absence from the final examination shall be considered incomplete with the letter grade “I”.

(c) Grade Point Average (GPA) and Total Credit Point (TCP): The weighted average of the grade points obtained in all the courses by a student and Total Credit Point shall be calculated from the following equations:

GPA = Sum of [(CP)i x (GP)i] / Sum of (CP)i

and

TCP = Sum of (CP)i

where (GP)i = grade point obtained in individual course, (CP)i = credit point for respective course, GPA = Grade Pont Average obtained and TCP = Total Credit Point obtained. GPA shall be rounded off up to 2 (two) places after decimal to the advantage of the examinee. For instance, GPA = 2.112 shall be rounded off as GPA = 2.12.

An illustration of calculating GPA and CGPA: Suppose a student has completed six courses in M.Sc. examination and obtained the following grades:

M.Sc. Course / Credits (CP) / Letter Grade (LG) / GP
501 / 4 / A / 3.75
502 / 4 / A+ / 4.00
503 / 4 / B+ / 3.25
504 / 4 / B- / 2.75
505 / 4 / C / 2.25
506 / 4 / F / 0.00

His/her GPA is: 2.67

and LG corresponding to GPA = 2.67 is “B-”

Award of Degree, Promotion and Improvement of Results:

(a) Award of Degree: The degree of Master of Science in any subject shall be awarded on the basis of GPA obtained by a candidate in M.Sc. In order to qualify for the M.Sc. degree a candidate must have to obtain within 3 (three) academic years from the date of first admission:

(i) A minimum GPA 2.50

(ii) A minimum GP of 2.00 in the Practical/Thesis, and

(iii) A minimum TCP of 36

The result shall be given in GPA with the corresponding LG (Table of LG, GP and CP) in bracket. For instance, in the example cited above the result is “GPA=2.67 (B-)”

(b) Publication of Results: The result of a successful candidate shall be declared on the basis of GPA. The transcript in English shall show the course number, course title, credit, grade and grade point of individual courses, GPA and the corresponding LG.

A candidate obtaining a GPA of less than 2.75 at the examination shall be allowed to improve his/her result, only once as an irregular candidate within 3 academic years from the date of first admission.

The year of examination, in the case of a result improvement, shall remain same as that of the regular examination. His/ her previous grades for Practical courses, Class assessment/Tutorial/Terminal/Home Assignment, Thesis/Dissertation/Project shall remain valid (except the Theory Viva-Voce). If a candidate fails to improve GPA, the previous result shall remain valid.

Detail Syllabus for M.Sc. Program

CSE 501: Pattern Recognition

Lecture: 60 (Hours), Credit: 4, Full Marks: 100

Basics of pattern recognition: Introduction to pattern recognition, feature extraction, and classification.

Bayesian decision theory: Classifiers, Discriminant functions, Decision surfaces, Normal density and discriminant functions, discrete features

Parameter estimation methods: Maximum-Likelihood estimation, Gaussian mixture models, Expectation-maximization method, Bayesian estimation

Hidden Markov models for sequential pattern classification: Discrete hidden Markov models, Continuous density hidden Markov models, Viterbi algorithm, Baum-Welch algorithm

Dimension reduction methods: Principal component, Fisher discriminant analysis

Non-parametric techniques for density estimation: Parzen-window method, K-Nearest Neighbour method

Linear/non-linear discriminant function based classifiers: Multi-layer Perceptron’s, Support vector machines

Non-metric methods for pattern classification: Non-numeric data or nominal data, Decision trees

Unsupervised learning and clustering: Criterion functions for clustering, Algorithms for clustering: K-means, Hierarchical and other methods, Cluster validation

References:

1. / R.O.Duda, P.E.Hart and D.G.Stork / : / Pattern Classification, John Wiley & Sons, 2001
2. / S.Theodoridis and K.Koutroumbas / : / Pattern Recognition, Academic Press
3. / C.M.Bishop / : / Pattern Recognition and Machine Learning, Springer
4. / E.G. Richard, Johnsonbaugh and S. Jost / : / Pattern Recognition and Image Analysis, Prentice Hall of India Private Ltd., NewDelhi

CSE 502: Network Design and Management

Lecture: 60 (Hours), Credit: 4, Full Marks: 100

Network Design: Design Principles, Determining Requirements, Analyzing the Existing Network, Preparing the Preliminary Design, Completing the Final Design Development, Deploying the Network, Monitoring and Redesigning, Maintaining, Design Documentation, Modular Network Design, Hierarchical Network Design, The Cisco Enterprise Composite Network Model.

Technologies - Switching Design: Switching Types, Spanning, Tree Protocol, Redundancy in Layer 2 Switched Networks, STP Terminology and Operation, Virtual LANs, Trunks, Inter VLAN Routing, Multilayer Switching, Switching Security and Design Considerations, IPv4 Address Design, Private and Public Addresses, NAT, Subnet Masks, Hierarchical IP Address Design, IPv4 Routing Protocols, Classification, Metrics, Routing Protocol Selection.

Network Security Design: Hacking, Vulnerabilities, Design Issues, Human Issues, Implementation Issues, Threats, Reconnaissance Attacks, Access Attacks, Information Disclosure Attacks, Denial of Service Attacks, Threat Defense, Secure Communication, Network Security Best Practices, SAFE Campus Design.

Wireless LAN Design: Wireless Standards, Wireless Components, Wireless Security, Wireless Security Issues, Wireless Threat Mitigation, Wireless Management, Wireless Design Considerations, Site Survey, WLAN Roaming, Wireless IP Phones, Quality of Service Design, QoS Models, Congestion Avoidance, Congestion Management.

Network Management: ISO Network Management Standard, Protocols and Tools, SNMP, MIB, RMON NetFlow, Syslog, Network Management Strategy, SLCs and SLAs, IP Service-Level Agreements, Content Networking Design, Case Study, Venti Systems.

References:

1. / D. Tiare and C. Paquet / : / Campus Network Design Fundamentals, Pearson Education.
2. / Craig Zacker / : / The Complete Reference: Upgrading and Troubleshooting Networks, Tata McGraw-Hill.
3 / D. L. Spohn, T. Brown and S. Grau, / Data Network Design, McGraw-Hill.
4. / William Stallings / : / Wireless Communications and Networks, Prentice Hall
5. / T. S. Rappaport / : / Wireless Communications, Pearson Education
6. / M. L. Liu / : / Distributed Computing: Principles and Applications, Pearson Education.
7. / R. Orfail, D. Harkey / : / Client/Server Programming with Java and CORBA, John Wiley and Sons, Inc.

CSE 503: Data Mining

Lecture: 60 (Hours), Credit: 4, Full Marks: 100

Introduction: Models, methodologies, and processes. The KDD process. Generic tasks, Application, Example: weather dataData

Warehouse and OLAP: Data Warehouse and DBMS, Multidimensional data model, OLAP operations, Example: loan data set

Data preprocessing: Data cleaning, Data transformation, Data reduction, Discretization and generating concept hierarchies, Experiments with Weka - filters, discretization

Data mining knowledge representation: Task relevant data, Background knowledge, Interestingness measures, Representing input data and output knowledge, Visualization techniques, Experiments with Weka - visualization