UNIVERSITIES OF COMPUTER STUDIES THE SYLLABUS FOR 2018 ...ucspyay.edu.mm/upload/syllabus/CS/fifth year-CS-combine.pdf · Academic & General Training (Handout) 2. CAMBRIDGE GRAMMAR

1

UNIVERSITIES OF COMPUTER STUDIES

THE SYLLABUS FOR 2018-2019 ACADEMIC YEAR

(Five-year-academic Plan)

English

Fifth Year ( B.C.Sc./ B.C. Tech.) / Final Year (B.C.Sc./ B.C. Tech.)

Objectives: To improve students’ proficiency in four skills and help them complete

preparation for the IELTS test

Students Learning Outcomes: Getting motivation in their language learning and applying

what they have learnt in their real life situation

Topic covered: Four skills with grammar, vocabulary input and skills practice to help

students to deal successfully with the tasks

Credit unit: 3 credits

(a) Course Description

1. Cambridge Grammar for IELTS

Unit 22 – Passive

2. Academic Writing Task 1: Describing a chart, table or graph; Comparing and

Contrasting graphs, tables; Describing diagrams

3. Reading Comprehension Passages (Teachers can use other authentic sources.)

(a) Syllabus

(b) Cambridge Grammar for IELTS

Unit 22 – Passive

(c) Academic Writing Task 1: Describing a chart, table or graph; Comparing and

Contrasting graphs, tables; Describing diagrams

(d) Reading Comprehension Passages(Teachers can use other authentic sources.)

(c) Textbook

1. Cambridge Grammar for IELTS: Grammar reference and practice by Diana

Hopkins and Pauline Cullen

2. Academic Writing

(d) References

1. Cambridge Grammar for IELTS: Grammar reference and practice by Diana

Hopkins and Pauline Cullen

2. Academic Writing

3. Supporting Materials from other authentic sources

*********************************************************************

Page 1 of 1

2018-19 Academic Year

English Department

Course Description

Fifth Year (First Semester)

Department Code: ENG

Subject Code: E 501

Course Title 1. The Official Cambridge Guide to IELTS for

Academic & General Training (Handout)

2. CAMBRIDGE GRAMMAR for IELTS

Course Coordinator Daw Aye Aye Khine

Credit Unit 3 credits ( lecture 2+ Tuto 1+ Lab 1)

1 Lecture = 1 credit

1 Tutorial= 0.5 credit

1 Lab = 0.5 credit

Prerequisite/s None

Objectives 1. to motivate students in English Language learning

2. to develop four skills in English Language learning

Student Learning Outcomes Students are motivated in their language learning and

are positioned right at the edge of their competence

are pushing it forward.

Topics Covered 1. Proficiency skill based on CLT

2. Four skills with grammar and vocabulary input are

informed by publications related to the Common

European Framework of Reference.

Text book and Reference 1. The Official Cambridge Guide to IELTS for

Academic & General Training (Handout)

2. CAMBRIDGE GRAMMAR for IELTS (By DIANA

HOPKINS with PAULINE CULLEN)

Lesson Plan First Semester - IELTS: Academic & General

Training Writing

- IELTS Grammar Unit 22

Assessment Plan First Term - Exam (50%)

Listening Test (10%)

Speaking (10%)

(Assignment) Writing (10%)

Attendance (10%)

Quiz (10%)

E English

The Official Cambridge Guide to IELTS for Academic & General Training

( handout)

No. Page Period

6 3.5

7 191-197 3.5

8 191-197 3.5

9 191-197 3.5

10 3.5

11 - 3.5

12 - 3.5

13 - 3.5

14 - 3.5

15 3.5

3.5

3.5

3.5

2.) Academic Writing Task 1 - Comparing and contrasting graphs, tables

2. Comparing and contrasting data

1.) Academic Writing Task 1 - Describing a chart, table or graph

97-101

102-105

106-108

108-109 3.51. Understanding a diagram

3. Lexical Resource - being accurate

2. Describing a process - chorence and cohesion

4 3.) Academic Writing Task 1 - Describing diagrams

1. Understanding a diagram

2. Describing a process - chorence and cohesion

Academic Writing Task 1 - Extra exercises

5 3.) Academic Writing Task 1 - Describing diagrams

93-97


LECTURE PLAN FOR 2018-2019 ACADEMIC YEAR

B.C.Sc. / B.C.Tech. Final Year

First Semester

Text Book : Grammar for IELTS

Period :15 weeks

3.5

Chapter

2. More complex charts

Academic Writing Task 1 - Extra exercises

3. Improving your Task Achievement score

1 1.) Academic Writing Task 1 - Describing a chart, table or graph

1. Understanding Graphs, tables and charts

Revision

Revision

1. Avoiding repetition

2.) Academic Writing Task 1 - Comparing and contrasting graphs, tables

3 Grammatical Accuracy - describing numbers and figures accurately

Grammar for IELTS: Unit 22 (The Passive)


Reading Passage (Teachers can use other authentic sources.)




2

3

E English

No. Page Period

1

2

3

4

5

6

7

8

9

10

11

12

13

14 Internship

Internship

Internship

Internship

Internship

Internship

Internship

Internship

Internship

Chapter

Internship

Internship

Internship

Internship

Internship


LECTURE PLAN FOR 2018-19 ACADEMIC YEAR

B.C.Sc. / B.C.Tech. Final Year

Second Semester

University of Computer Studies

B.C.Sc. / B.C.Tech. (Fifth Year)

CST-501

COURSE DESCRIPTION

Course code

number CST-501 Course Title Mathematics of Computing V

Semester hours 4 hours No. of Credit Units 3

Course Coordinator Daw Ni Ni Hla

Course Description

CST-501. Mathematics of Computing V

First part of this course, Markov Chains, covers stochastic process, Markov chains,

Chapman-Kolmogorov equation, classification of states of a Markov chains, long-run

properties of Markov chains, first passage times, absorbing states, continuous time

Markov chains.

Second part of this course, covers basics structure of queueing models, examples of real

queueing systems, the role of the exponential distribution, the birth and death process,

queueing models based on the birth and death process.

Last part of this course covers introduction to simulation, concepts in discrete-event

simulation (DES), components of DES, random number generation and performing

simulation.

Textbook

Introduction to Operations Research, 7th

Edition By Hillier/Liberman

Course Outcomes

The students who succeed the course, Markov Chains, will be able to:

1. Understand and apply Markov Chain to describe real problems,

2. Able to evaluate the steady-state performances, and

3. Understand the analysis techniques for studying Markov chains.

The students who succeed Queueing Theory course will be able to:

1. Understand the terminology and nomenclature appropriate to queueing theory

2. Demonstrate knowledge and understanding of various queueing models

3. Formulate concrete problems using queueing theoretical approaches

After completion of simulation course, students course will be able to understand basic

concepts involved in computer simulation of systems and able to learn and practice the

overall process of simulation models.



Major Topics Covered in the Course

1. Markov Chains

2. Queueing Theory

3. Simulation

Assessment Plan for the Course

Attendance - 10%

Quizzes - 10%

Assignment - 10 %

Test - 10%

Final Exam - 60%

Class Attendance and Participation Policy:

Attendance

Class attendance is mandatory. Most of the material you will learn will be covered in the

lectures, so it is important that you not miss any of them. You are expected to show up on time

for class, and stay for the whole lecture. Students are expected to attend each class, to complete

any required preparatory work (including assigned reading) and to participate actively in

lectures, discussions and exercises.

• Mobile phones must be silenced and put away for the entire lecture unless use is specified by

the instructor. You may not make or receive calls on your cell phone, or send or receive text

messages during lectures.

• You are responsible for all material sent as email. Ignorance of such material is no excuse. You

are responsible for all materials presented in the lectures.

• Your conduct in class should be conducive towards a positive learning environment for your

class mates as well as yourself.

Quizzes, assignments, tests and Exam

Your performance in this class will be evaluated using your scores for attendance,

quizzes, homework assignments, two tests and one final examination. There are no planned extra

credit projects or assignments to improve your grade.

We will take a short quiz for every lecture.

There will be 12 homework assignments, roughly one per week. Please show all your

work and write or type your assignments neatly. Credit cannot be given for answers without

work (except on true-false, always-sometimes-never, or other multiple choice questions).



Test will start after two or three chapters finished and the coordinator will announce the

date for the test.

Any assignment or quiz or test is simply missed, regardless of the reason why (e.g.

illness, work, traffic, car trouble, computer problems, death, etc.), and earns a grade of zero.

You are strongly encouraged to complete all assignments and attend all quizzes so that you can

check that you understand the material and can throw out bad grades, or grades for which you

had to miss an assignment or quiz for a valid reason. Late submissions will not be accepted for

any graded activity for any reason.

There are no extra credit opportunities.

Students may not do additional work nor resubmit any graded activity to raise a final

grade.

Exam

The exam will be conducted on-campus, in a classroom. The dates/times/locations will be

posted on Board as soon as possible.

For this course, the following additional requirements are specified:

All work submitted for a grade must have been prepared by the individual student. Students are

expressly prohibited from sharing any work that has been or will be submitted for a grade, in

progress or completed, for this course in any manner with a person other than the instructor and

teaching assistant(s) assigned to this course). Specifically, students may not do the following,

including but not limited to:

Discuss questions, example problems, or example work with another person that leads to

a similar solution to work submitted for a grade.

Give to, show, or receive from another person (intentionally, or accidentally because the

work was not protected) a partial, completed, or graded solution.

Ask another person about the completion or correctness of an assignment.

Post questions or a partial, completed, or graded solution electronically (e.g. a Web site).

All work must be newly created by the individual student for this course. Any usage of

work developed for another course, or for this course in a prior semester, is strictly

prohibited without prior approval from the instructor.

Posting or sharing course content (e.g. instructor provided lecture notes, assignment

directions, assignment questions, or anything not created solely by the student), using any

non-electronic or electronic medium (e.g. web site, FTP site, any location where it is



accessible to someone other than the individual student, instructor and/or teaching

assistant(s)) constitutes copyright infringement and is strictly prohibited without prior

approval from the instructor.

Tentative Lesson

No Topics Week Remark

I Chapter 16 - Markov Chains

1 16.1 Stochastic Process Week 1

2 16.2 Markov Chains Assignment 1

3 16.3 Chapman-Kolmogorov Equation Week 2

4

16.4 Classification of States of a

MarkovChains Assignment 2

5

16.5 Long-Run Properties of Markov Chains Week 3 Assignment 3

6 16.6 First Passage Times Week 4

7 16.7 Absorbing States Assignment 4

8 16.8 Continuous Time Markov Chains Week 5

9 Test I

II Chapter 17 - Queueing Theory

10 17.1 Prototype Example Week 6

11 17.2 Basics Structure of Queueing Models Assignment 5

12 17.3 Examples of Real Queueing Systems Week 7-9 Assignment 6

13 17.4 The Role of the Exponential Distribution Assignment 7

14 17.5 The Birth and Death Process Week 10-

12 Assignment 8

15

17.6 Queueing Models Based on the Birth and

Death Process Assignment 9

16 Test II

III Chapter 22 - Simulation

17 22.1 Essence of Simulation Week 13 Assignment 10

18

22.2 Some common types of applications of

simulation

Week 14

19 22.3 Generation of Random Numbers Assignment 11

20

22.4 Generation of Random Observations

from A Probability Distribution

Week 15

21 22.5 Outline of A Major Simulation Study

22

22.6 Performing Simulations on

SpreadsheetsProblems Assignment 12

23 Revision

Final Exam

University of Computer Studies, Yangon

Faculty of Information Science


Department Name FIS Course Title CS-503 (Information Assurance

and Security)

Semester hours

4 Periods per week

(2 for Theory & 2 for Lab)

(15 weeks)

Course

Coordinator

Dr. Nyein Myint Myint

Aung

Lecturer

Semester First Semester

Course Description

This course aims to motivate the topic of information security for final year students. After

completing the detailed look at the basic components of general information security mode,this course is

focused on helping students acquired the skills sough in the professional workforce.

At the outset, system administration introduces the basics setup and usage of the virtual machine

(distribution of the CentOS Linux OS to be installed as a virtual machine using virtual box). The instructions

are detailed enough for students to be able to complete the exercises on their own. After this, the

fundamentals of encryption technologies are described as security control. The rest of the course is devoted

to how to identify and manage their privileges in enterprise systems and what are the most essential and best

known controls. Finally, the course wrap up many of the concepts and ideas reviewed in the past chapters

into the narrative of an incident via incident handling and incident analysis.Finally, the course will also step

away the students from the technical world and discuss administrative mechanisms (policies, standards and

guidelines) available to security analysts and system administrators.

At the end of the course, students will have an awareness of how information security concerns have

evolved in our society and how they can use contemporary frameworks to respond to these concerns in a

professional environment.The book comes with a full set of end-of-chapter exercises. There are four kinds of

exercises at the end of every chapter:

1.Traditional end of chapter questions are designed to improve student understanding and recall of common

topics in information security.

2.An example case at the end of each chapter allows students to apply the knowledge in the chapter to

business contexts.

3.A critical thinking exercise introduces students to analogous situations and relates the ideas from the

chapter to these situations.

4.Finally each chapter has a detailed hands-on activity using a customized distribution of the CentOS Linux

OS to be installed as a virtual machine using virtual box.

Learning Outcomes

Students who complete the course will be able to

Understand how information security and assurance is important and impact of

organizations, and the importance of system administration for information security.

Design, implement, test and debug a BASH shell script that uses each of the following

fundamental programming construct and system administrative configurations: basic

computation, simple I/O, standard conditional and iterative structures, methods and

parameter passing and handling user interaction and the usage of common UNIX tools to

parse and manipulate text files.

Test and debug their script by: Developing test cases and output redirecting to discover their

program’s behavior.

Understand the standard, practical implementation of encryption technologies used in

information exchange, and PKI infrastructure to make encryption convenient and practical.

Understand the strengths and weaknesses of the major authentication technologies, and

information security control best practices.

Identify the major components of dealing with an incident, the incident handling lifecycle.

Prepare a basic policy outlining a methodology for the handling of an incident.

Develop a better understanding on how to protect our information assets and defend against attacks,

as well as how to apply these concepts practically.

Help to develop better security practices for any organizations in real world practice.

Drive security projects and policies, in order to mitigate some of the issues which are usually

encountered in any organization.

Prerequisites

Basic Information Security Model

The fundamental concepts to information security and assurance.

Major Topic Covered in the Course

System Administration

Encryption Controls

Identity and Access Management

Hardware and Software Controls

Shell Scripting

Incident Handling

Incident Analysis

Policies, Standards, and Guidelines

IT Risk Analysis and Risk Management

Textbook

“INFORMATION SECURITY and IT RISK MANAGEMENT”, 1st Edition, Wiley. Manish

Agrawal, Alex Campoe and Eric Pierce, 2014

Reference Book

“INFORMATION SECURITY: The Complete Reference”, 2nd

Edition, McGraw Hill,

Mark Rhodes-Ousley, 2013

“Network Security : A Beginner’s Guide”, 3rd

Edition, Eric Maiwald,

ISBN:9780071785715, McGraw-Hill, 2013

Learning Assessment

Paper Exam : 60%

Tutorial Test : 10%

Project : 10%

Practical Assessment : 10%

Assignment : 5%

Class Participation : 5%

Team Project Description

The project topics can be choose from the following topics but not limited to:

1. SQL Injection

2. Password (Password hacking tools)

3. Network Scan

4. Intrusion Detection (Host-based intrusion detection system)

5. Cross-site scripting

6. Cross-site request forgery

7. Key logger

8. Social engineering (with scenario)

9. Packet sniffing

10. Network monitoring tools

11. Denial of service (DOS)

12. Phishing

13. Buffer overflow

14. Unrestricted Uploads

15. Vulnerability Assessment tools (OpenVas, Acunetix, Vega, etc.)

16. Shell script security

17. Pretty good privacy (pgp)

18. Secure emailing

19. Multi-factor authentication

20. Steganography (at least 5 different ways in demonstration)

Presentation and demonstration time is grant 20 minutes, questions and answers session 10

minutes.

To get started on your project, your assignment consists of the following tasks:

Project Outlines

1. Title

2. Project Group Members

3. Resource Assignment of the tasks

4. Description

5. Objective & Motivation

6. Project Concerned Contents

7. Practical Demonstration

8. Counter-measures

9. Conclusion

Course Policy

Attendance is not mandatory but highly recommended.

Individual deliverables are to be submitted individually and group work is collaborative.

All exams and assignments are to be completed by the student alone with no help from any

other person.

Students are allowed to discuss about homework and project problems with others.

Students are not allowed to copy the solutions from another colleague.

If so, all students (with same answers) must be deducted 1% of their marks(assignment).

If you hand in a late assignment, you must identify (1) how late this assignment is and (2)

how many total slip days you have left.

After you have used up your slip time, any assignment handed in late will be marked off

0.5% per day. That is, after 5 days, the mark must be zero.


B.C.Sc.(Fifth Year)

CS-503 - Information Security and Assurance

Textbook - Information Security and IT Risk Management

by ManishAgrawal, Alex Campoe, Eric Pierce

Periods - 60 periods (30 periods for Lecture, 30 periods for Lab)

No. Chapter Page Period Detailed

Lecture

1. Chapter 3–System Administration 6 Practical

Oriented

Overview, Operating system structure,

The command-line interface, Files and directories

Moving around the file system

2 Explain Details

Listing files and directories, shell expansions

File management, viewing files, Searching for files

2

Access control and user management, Access control lists

File ownership, Editing files

Command-line user administration

2 Explain Details

2. Chapter 7 - Encryption Controls 176-206 4

Encryption Basics

Encryption Types Overview

2 Explain Details

Example Case – Nation Technologies

Chapter Review Questions

Hands-On Activity (Encryption)

Critical Thinking Exercise

2 Assignment

Lab

Assignment

3. Chapter 8 - Identity and Access Management 207-245 6

Identity Management

Access Management

2 Explain Details

Authentication 2 Explain Details

Example Case - Markus Hess


2 Assignment

Hands-On Activity (Two-factor authentication)


Lab

Assignment

4. Chapter 9 - Hardware and Software Controls 251-275 7

Access Control

Firewall

2 Explain Details

Intrusion Detection/Prevention Systems

Patch Management for Operating Systems and

Applications

3 Explain Details

Example Case - AirTight Networks


Hands-On Activity (OSSEC)


2 Assignment

Lab

Assignment

5. Chapter 10 – Shell Scripting 277-304 16 Practical

Oriented

Introduction

Output Redirection

Text Manipulation

Variables

1

1

1

1

Explain Details

Conditionals

User Input

Loop

Put It All Together

1

1

2

1

Explain Details

Hands-On Activity

7

Lab

6. Chapter 11 - Incident Handling 306-331 5

Incidents Overview

Incident Handling

The disaster

4 Explain Details

Explain Details

Definition

Example Case – on-campus piracy



1 Assignment

Assignment

7. Chapter 12 - Incident Analysis 333-358 5

Log Analysis

Event Criticality

2 Lab

General Log Configuration and Maintenance

Live Incident Response

2

Example Case - Backup Server Compromise



1 Assignment

Assignment

8. Chapter 13 - Policies, Standards and Guidelines 360-381 6

Guiding Principles

Key Policy Issues

4

1

Example Case - HB Gary



1 Assignment

Assignment

9. Chapter 14 – IT Risk Analysis and Risk Management 382-401 5

Risk Management as a Component of Organizational

Management

The NIST 800-39 Framework

Risk Assessment

4

Example Case - Online Marketplace Purchases

Chapter Review Question

1

Assignment

CS-504 (Computing Applied Algorithms) (Elective)

Course Description

Course code

number

CS-504(AA)

First

Semester

Course Title Computing Applied Algorithms


Prerequisite CS-403, CST-

103, CST-203 Course Coordinator Dr. Khin Mar Soe, Professor

Natural Language Processing Lab

Course Aims

The main purpose of this course is to know data structures for set manipulation problems and

the concept of algorithms on graph. In addition, the students can learn the fundamental concepts

and techniques of parallel computing and to know how to design and analyze parallel

algorithms.

Learning Outcomes

Upon successful completion of the course the student:

1. will know the structure and properties of a binary search tree and optimal binary search

tree

2. will be familiar with the some of the principal graph problems

3. will be familiar with the concepts of parallel processing and understand the particular

problems arising in programming of parallel machines;

4. will be familiar with the parallel computing models and the “parallel-way of thinking”

required in the design of parallel algorithms;

5. will be able to use the metric of running time, number of processors, cost, speed-up and

efficiency to analyze the performance of given parallel algorithms and compare between

them

6. will be able to understand and use basic sorting and merging parallel algorithms

Course Contents

The study of algorithms is at the very heart of computer science. In this course students will

learn

1. Algorithms using Tree Data Structures: Binary Search Trees, Optimal Binary Search

Tree

2. Algorithms using Graphs : Minimum Cost Spanning Tree, Depth-first-search

(undirected & directed Graph), Path-finding problem, A transitive closure algorithm, A

shortest-path algorithm

3. Parallel Computing : The need for parallel computer, Models of Parallel Computation

(SISD, MISD, SIMD, MIMD), Analyzing Algorithms (Running Time, Number of

processors, Cost)

4. Algorithms for Parallel Merging: Merging on the CREW Model ( Sequential Merging

& Parallel Merging ), Merging on the EREW Model, A better algorithm for the EREW

Model

5. Algorithms for Parallel Sorting: Sorting on the CRCW Model, Sorting on the CREW

Model Sorting on the EREW Model

Course Organization

The expected learning outcomes for the course will be assessed through six forms of

activity:

1. Attending the lectures

2. Preparing for and participating in the recitations.

3. Assignments

4. Reading the text

5. Quiz

6. Exams

Reference Materials:

1. The Design and Analysis of Computer Algorithms by Alfred V. Aho, John E.Hopcroft &

Jeffery D. Ullman

2. The Design and Analysis of Parallel Algorithms by Selimm G. Akl

Referential Sources

https://computing.llnl.gov/tutorials/parallel_comp/

https://www.geeksforgeeks.org/binary-search-tree-set-1-search-and-insertion/

https://www.tutorialspoint.com/data_structures_algorithms/spanning_tree.htm

Exam Assessment

Paper Exam 50%

Tutorial 10%

Assignment + Attendances 10%

Practical Assignment 10%

Quiz + Moodle 10%

Project 10%

https://computing.llnl.gov/tutorials/parallel_comp/

https://www.geeksforgeeks.org/binary-search-tree-set-1-search-and-insertion/

https://www.tutorialspoint.com/data_structures_algorithms/spanning_tree.htm

Period : 45 Periods for 15 weeks (50 minutes for 1 period)

No. Title & Contents Credits Reference Book &

Chapter

1 Subject Introduction 1 All Chapters

Book (1& 2)

2 Algorithms using Tree Data Structure & Graphs 18 Book (1)

Binary Search Trees 1 Chapter 4, Section 4.4

Optimal Binary Search Trees 3 Chapter 4, Section 4.5

Minimum Cost Spanning Trees 2 Chapter 5, Section 5.1

Depth-first-search ( undirected graph) 2 Chapter 5, Section 5.2

Depth-first-search( directed graph) 2 Chapter 5, Section 5.4

Biconnnectivity 2 Chapter 5, Section 5.3

Strong connectivity 2 Chapter 5, Section 5.5

Path-finding problem 2 Chapter 5, Section 5.6

A transitive closure algorithm 1 Chapter 5, Section 5.7

A shortest-path-algorithm 1 Chapter 5, Section 5.8

3 Parallel Computing 7 Book (2)

The need for parallel computer,

Models of Computation (SISD Computers)

1 Chapter 1

Section 1.1, 1.2, 1.2.1

Models of Computation (MISD Computers) 1 Chapter 1,

Section 1.2.2

Models of Computation(SIMD) 3 Chapter 1

Section 1.2.3

Models of Computation(MIMD),

Analyzing Algorithms

Running Time

Number of processors Cost

2 Chapter 1

Section 1.2.4, 1.3

4 Merging 9

Merging on the CREW Model

Sequential Merging, Parallel Merging

3 Chapter 3

Section 3.3

Merging on the EREW Model 3 Chapter 3

Section 3.4

A better algorithm for the EREW Model

Finding the median of two sorted

sequences

Fast Merging on the EREW Model

3 Chapter 3

Section 3.5

5 Sorting 7

Sorting on the CRCW Model 1 Chapter 4, Section 4.4

Sorting on the CREW Model 3 Chapter 4, Section 4.5

Sorting on the EREW Model 3 Chapter 4, Section 4.6

6 Mini Project Presentation 3

CS-504 (Computer Vision and Interactive Computer Graphics) (Elective)

Course Description

Course code

number

CS-504

First

Semester

Course Title Computer Vision and Interactive

Computer Graphics


Prerequisite CS-406, CST-

203, Maths Course Coordinator Dr. Ah Nge Htwe, Associate Professor

Faculty of Computer Science

Course Aim

The aim of this course is to provide the overview of computer vision techniques, the

concepts of 3D computer graphics and practical implementation using OpenGL graphics

library.

Course Objectives

• Identify and explain the core concepts of 3D computer graphics.

• Apply graphics programming techniques to design, and create computer graphics

scenes.

• Create effective OpenGL programs to solve graphics programming issues, including

3D transformation, animation and color modeling.

Learning Outcomes

On successfully completing the course, students will be able to demonstrate

knowledge and understanding of fundamental principles in 3D computer graphics, and apply

them to the design of algorithms for graphics applications. Students will be able to develop

OpenGL programs to model various types of three-dimensional scenes and animations.

Students will also gain skills necessary for the study of advanced concepts and techniques in

the field of computer graphics.

Course Contents:

3D Concepts : 3D basic concepts and Stereoscopic Views

Three-Dimensional Object Representations : Curved Lines andPolygon Surfaces

Three-Dimensional Geometric and Modeling Transformations : basic transformation

Color Models and Color Applications : properties of light and color model

Computer Animation : General Computer-Animation Functions and key frame system

Visible-Surface Detection Methods : Depth-Buffer Method and A-Buffer Method

Introduction to Computer Vision and Recognition:A brief history of computer vision

and face detection.


1. Computer Graphics C Version, Second Edition, Donald Hearn and M. PaulineBaker,1997.

2. Computer Vision: Algorithms and Applications, Richard Szeliski, 2010.

3. Computer Graphics with OpenGL, Fourth Edition (Pearson New International Edition),

Hearn, Baker and Carithers, 2014.

4. OpenGL Programming Guide, Eighth Edition, Dave Shreiner, Graham Sellers, John

Kessenich and Bill Licea-Kane, 2013.

Course Organization

Your participation in the course will involve six forms of activity:

1. Attending the lectures.


3. Laboratory assignments.

4. Reading the text.

5. Exams

6. Quiz

Exam Assessment

Paper Exam 50%

Practical 10%

Tutorial 10%

Assignment 10%

Attendance 10%

Quiz and Moodle test 10%

Period : 60 Periods (55 minutes) for 15 Weeks

No. Chapter Page Period

1 Chapter 9

Three-Dimensional Concepts

2 9-1 Three-Dimensional Display Methods

Three-Dimensional and Stereoscopic

Views

297

300 1

3 Chapter 10

Three-Dimensional Object Representations

10.1 Polygon Surfaces

10.2 Curved Lines and Surfaces

305-

312 2

10.3 Quadric Surfaces

Practical + Tutorial + Discussion 3

Chapter 11

Three-Dimensional Geometric and Modeling

Transformations

4 11-1 Translation

11-2 Rotation

408 -

413 2

5 11-2 General 3D Rotation

Rotation with Quaternions

414-

420 2

11-3 Scaling

11-4 Other Transformations

420-

423 4

6 Discussion 2

7 Practical + Tutorial 6

8 Chapter 15

Color Models and Color Applications

9 15-1 Properties of Light

15-3 Intuitive Color Concepts

565-

571

2

10 1 5-4,5,6 Color Model

1 5-10 Color Selection and Applications

572-

580 4

Practical + Tutorial 4

11 Discussion 2

12 Chapter 16

Computer Animation

13 16-1 Design of Animation Sequences

16-2 General Computer-Animation Functions

584-

586

2

14 16-3 Raster Animations

16-4 Computer-Animation Languages

16-5 Key-Frame Systems Morphing

586-

588 4

Practical + Tutorial 4

15 Discussion 2

16 Chapter 13

Visible-Surface Detection Methods

17 13-1 Classification of Visible-Surface Detection

Algorithms

13-2 Back-Face Detection

13-3 Depth-Buffer Method

13-4 A-Buffer Method

13-5 Scan-Line Method

13-12 Wireframe Methods

470 –

476,

490

4


Discussion 1

19 Chapter 1 Introduction to Computer Vision

20 1.1 What is computer vision?

1.2 A brief history

3

10 1

21 Chapter 14 Recognition (Overview)

22 14.1 Object detection

14.2 Face recognition

14.3 Instance recognition

14.4 Category recognition

14.5 Context and scene understanding

14.6 Recognition databases and test sets

658

668

685

696

712

718

2


24 Discussion 1

Page 1 of 3

CS-504 (Advanced Artificial Intelligence (Natural Language Processing)) (Elective)

Course Description

Course code

number

CS-504(AA)

First

Semester

Course Title Natural Language Processing


Prerequisite

computer

science

background

Course Coordinator Dr. Khin Mar Soe, Professor

Natural Language Processing Lab

Course Aims

The main purpose of this course is

To understand natural language processing and to learn how to apply basic algorithms

in this field.

To get acquainted with the algorithmic description of the main language levels:

morphology, syntax, semantics, and pragmatics, as well as the resources of natural

language data - corpora.

To conceive basics of knowledge representation, inference, and relations to the

artificial intelligence.

Learning Outcomes

By the end of the course, the student must be able to:

Compose key NLP elements to develop higher level processing chains

Understand the concept of regular expression

Learn commonly used operations involving regular expression/pattern matching

Choose appropriate solutions for solving typical NLP subproblems (tokenizing,

tagging, parsing)

Measure of how alike two strings are to each other

Course Contents

The goal of natural language processing (NLP) is to design and build computer systems

that are able to analyze natural languages like German or English, and that generate their

outputs in a natural language, too. In this course students will learn

1. Knowledge in Speech and Language Processing, ambiguity of Language

2. Regular expression and Finite-State Automata with examples

Page 2 of 3

3. Survey of English Morphology and Finite-State Morphological Parsing

4. How to build a Finite-State Lexicon and Finite-State Transducers

5. Tokenization of word and sentence and detecting, correcting of spelling errors

6. Examples of string edit distance

Course Organization

The expected learning outcomes for the course will be assessed through six forms of

activity:

1. Attending the lectures


3. Assignments

4. Reading the text

5. Quiz

6. Exams


1. Daniel Jurafsky and James H. Martin, Speech and Language Processing An Introduction

to Natural Language Processing, Computational Linguistics and Speech Recognition ,

Prentice Hall; 2nd edition (May 16, 2008) , ISBN-13: 978-131873216, ISBN-10:

0131873210

2. Christopher D. Manning, Hinrich Schütze , Foundations of Statistical Natural Language

Processing, The MIT Press; 1st Edition (June 18, 1999), ISBN-10:0262133601, ISBN-13:

978-0262133609

Exam Assessment (AI 70%+NLP 30%)

1. Paper Exam 10%

2. Tutorial 10%

3. Assignment + Attendances 10%

Periods : 15 periods for 15 weeks (50 minutes for 1 period)

No. Chapter Pages Periods Detail Lectures

1 Chapter 1 Introduction 1-15 2

1.1 Knowledge in Speech and Language

Processing

1.2 Ambiguity of Language: why NLP is

difficult?

1.3 Models and Algorithms

2 Overview

Page 3 of 3

1.5 state of the art

1.6 Some brief history

2 Chapter 4 N-grams 93-127 7

4.1 Counting words in corpora

4.2 Simple N-grams

4.3 Training and Test Sets

4.4 Evaluating N-grams

4.5 Smoothing

1

1

1

1

1

Detail

Detail

Detail

Detail

Detail

Exercises and Assignments 2 N-gram modeling

3 Chapter 5 Word Classes and Part-of-Speech

Tagging + Chapter 6 Hidden Markov and

Maximum Entropy Model

137-

206

5

5.1 English Word Classes

5.2 Tag-sets for English

5.3 Part of Speech Tagging

5.4 Rule based Part of Speech Tagging

1

1

1

1

Overview

Overview

Detail

Overview

. Reading assignment and Discussion/presentation

of the current POS tagging approaches

1

Tutorial I 1 All Chapters




Course Description

The course is designed to expand students’ knowledge and skill gained in database

management courses and look in depth at data warehousing and data mining methods. The course

examines the database architectures and technologies required for solving complex problems of data

and information management, information retrieval and knowledge discovery facing modern

organizations. This course also provides hands-on experience with state-of-the-art data mining

methods tools using WEKA software.

Course Objectives:

This course is intended to

Introduce the basic concepts and techniques of data mining.

Develop skills of using recent data mining software for solving practical problems.

Gain experience of doing independent study and research

Study the methodology of engineering legacy database for data mining to derive business

rules for decision support systems.

Develop and apply critical thinking, problem solving and decision making skills.

Learning Outcomes:

Students who complete the course should be able to

Know the latest development of knowledge discovery and data mining concepts and

techniques.

Utilize the theories and algorithms for data mining and knowledge discovery.

Manipulate the possibilities and fundamental limitations that are included in data

preprocessing steps of data mining.

Department

Name FIS Course Title

CS-505

Data Mining Concept and

Technique

(Elective)

Semester

hours

4 Periods per week

(2 for Lecture, 2 for Lab)

(15 weeks)

Course

Coordinator

Daw Khaing

Associate Professor

Prerequisite CST-204, CS-304

CS-404

Analyze and compare the performance of different mining methods on a wide range of

datasets using data mining methods such association, classification and clustering analysis.

Apply the data mining method using WEKA tools for analysis.

Implement the relevant applications in specific domains such as medicine and health care,

market basket analysis, etc.

Course Contents

1. Introduction to Data Mining

2. Getting to know your data

3. Data Preprocessing

4. Mining frequent patterns, Associations and Correlations, Basic Concept and Method

5. Classification Basic Concept

6. Cluster Analysis: Basis Concept and Method

Tools

WEKA software

Text Book

Jiawei Han, MichelineKamber and Jian Pei, Data Mining – Concepts and Techniques.

Morgan Kaufmann, Third Edition, 2011.

Reference Books

Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison

Wesley, 2006.

Ian H. Witten and Eibe Frank, Data Mining – Practical Machine Learning Tools and

Techniques (2nd Ed.), Morgan Kaufmann, 2005.

S.M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998.

Margaret H. Dunham, Data Mining – Introductory and Advanced Topics, Prentice Hall,

2003.

Learning Assessment

Paper Exam : 60%

Tutorials/Test : 10%

Quiz/Discussion : 10%

Project /Presentation : 10%

Assignment : 5%




B.C.Sc (Fifth Year)

CS-505 : Data Mining (First Semester)

Text Book : Data Mining Concepts and Techniques (Jiawei Han, MichelineKamber,

JianPei)

Period - 75 periods for 15 Weeks including Practice (50 minutes for 1 period)

No. Chapter Page Period Remark

1 1 Introduction 1 2

1.1 Why Data Mining? 1

1.2 What is Data Mining? 5

1.3 What Kinds of Data Can Be Mined? 8 2

1.4 What Kinds of Patterns Can Be Mined? 15 2

1.5 Which Technologies Are Used? 23 1

1.6 Which Kinds of Applications Are Targeted? 27 1

1.7 Major Issues in Data Mining 29 2

1.8 Summary 33 1

1.9 Exercises 34

2 2 Getting to Know Your Data 39

2.1 Data Objects and Attributes Types 40 2

2.2 Basic Statistical Description of Data 44 3

2.3Data Visualization 56 2

2.4 Measuring Data Similarity and Dissimilarity 65 3

2.5 Summary 79 1

2.6 Exercises 79

3 3 Data Preprocessing 83

3.1 Data Preprocessing: An Overview 84 2

3.2 Data Cleaning 88 2

3.3 Data Integration 93 2

3.4 Data Reduction 99 2

3.5 Data Transformation and Data Discretization 111 2

3.6 Summary 120 2

3.7 Exercises 121

4 6 Mining Frequent Patterns, Associations, and

Correlation: Basic Concepts and Methods

243

6.1 Basic Concepts 243 2

6.2 Frequent Itemset Mining Methods 248 5

6.3Which Patterns Are Interesting? Pattern Evaluation

Methods

264 3

6.4 Summary 271 2

6.5 Exercises 273

5 8 Classification : Basic Concepts 327

8.1 Basic Concepts 327 2

8.2 Decision Tree Induction 330 2

8.3 Bayes Classification Methods 350 2

8.4 Rule-Based Classification 355 2

8.5 Model Evaluation and Selection 364 2

8.6 Techniques to Improve Classification Accuracy 377 2

8.7 Summary 385 2

8.8 Exercises 386

6 10 Cluster Analysis: Basic Concepts and Methods 443

10.1 Cluster Analysis 444 2

10.2 Partitioning Methods 451 2

10.3 Hierarchical Methods 457 2

10.4 Density-Based Methods 471 2

10.5 Grid-Based Methods 479 2

10.6 Evaluation of Clustering 483 2

10.7 Summary 490 2

10.8 Exercises 491

1




Department

Name FIS Course Title

CS-505

Web Engineering

(Elective)

Semester hours

4 Periods per week (2 for Lecture, 2 for Lab) (15 weeks)

Course

Coordinator

Dr. Khine Khine

Oo

Professor

Semester First Semester

Course Description

This course introduces students to the discipline of Web Engineering including the

methods and techniques used in web-based system development. Web Engineering introduces a

structured methodology utilized in software engineering to Web development projects. The

course address the concepts, methods, technologies and techniques of developing Web sites that

collect, organize and expose information resources.

Course Objectives

The objective of this course is to provide students with a solid understanding of a

pragmatic process of engineering Web-based systems and application. This course emphasizes

an agile process and simple, practical methods that have been proven in industry application.

Learning Outcomes:

On successful completion of the course students will be able to:

Apply the web engineering methodologies for Web application development

Understand the generic Actions and Tasks for the Web Engineering framework

Gain the knowledge of incremental nature of Web Engineering Process.

Know the analysis models that allow the WebApplication requirements to be analyzed in

a structured manner

Get a clear understanding of the characteristics that make a good design

Understand the interplay nature between construction and deployment activities in Web

Engineering framework

Prerequisite

2

Basic Web Technologies

Software Engineer concepts

Design concepts

Web languages

Course Contents

1. Web-Based Systems

2. Web Engineering

3. Web Engineering Process

4. Communication

5. Planning

6. The Modeling Activity

7. Analysis Modeling for Web Apps

8. Web App Design

9. Construction and Deployment

References Materials

TextBook

1. Roger Pressman, David Lowe, “ Web Engineering: A Practitioner’s Approach” , 1st

Edition, 2009.

2. Ian Sommerville, “Software Engineering: A Practitioner’s Approach”, 6th Edition, 2005

Tools

1. Laravel Framework

2. Web Site Creation Tools (Joomla, Wordpress)

Learning Assessment

Paper Exam : 60%

Quiz/Tutorial : 10%

Assignment : 10%

Project : 10%


1

Lecture Plan Details

CS-505 : Web Engineering First Semester

Text Book : Web Engineering: A Practitioner’s Approach (Roger Pressman)

Periods : 60 periods for 15 Weeks ( 4 periods * 1 week)

No. Chapter Page Period Remark

WebE Chapter 1 : Web-Based Systems 1

The Web 1

Web Applications 1

Web Apps- A Philosophical View 1

Chapter review/ Tutorial/Assessment 1

WebE Chapter 2: Web Engineering 12

What is Web Engineering? 1

The Components of Web Engineering 1

Web Engineering Best Practices 1


WebE Chapter 3: A Web Engineering Process 24

Defining the Framework 1

Incremental Process Flow 1

Generic Actions and Tasks for the WebE Framework 1

Umbrella Activities 1


WebE Chapter 4: Communication 46

The Communication Activity 1

Formualtion 1

2

Elicitation 1

Identifying WebApp increments 1

Negotiation 1


WebE Chapter 5: Planning 70

Understanding Scope 1

Refining Framework Activities 1

Building a WebE Team 1

Managing Risk 1

Developing Schedule 1

Managing Quality 1

Managing Change 1

Tracking the project 1

Outsourcing WebE Work 1


WebE Chapter 6: The Modeling Activity 109

The Modeling Activity 1

The Models We Create 1

Modeling Frameworks 1

Modeling Languages 1

Existing Modeling Approaches 1


WebE Chapter 7:Analysis Modeling for WebApps 129

Understanding Analysis in the Context of WebE 1

3

Analysis Modeling for WebApps 1

Understanding the Users 1

The Content Model 1

The Interaction Model 1

The Functional Model 1

The Configuration Model 1

Relationship Navigation Analysis 1


WebE Chapter 8: WebApp Design 165

Design for WebApp 1

Design Goals 1

Design and WebApp Quality 1

The Design Process 2

Initial Design of the Conceptual Architecture 1

Initial Design of the Technical Architecture 1


WebE Chapter 12: Construction and Deployment 296

Construction and Deployment Within the WebE

Process 1

Construction 1

Construction Principles and Concepts 1

Deployment 1

Construction and the Use of Components 1

Component- Level Design Guidelines 1

4

Component Design Steps 1


Revision for all Chapters

Documents

UNIVERSITIES OF COMPUTER STUDIES THE SYLLABUS FOR 2018 ...ucspyay.edu.mm/upload/syllabus/CS/fifth year-CS-combine.pdf · Academic & General Training (Handout) 2. CAMBRIDGE GRAMMAR