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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
UNIVERSITIES OF COMPUTER STUDIES
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)
Grammar for IELTS: Unit 22 (The Passive)
Reading Passage (Teachers can use other authentic sources.)
Reading Passage (Teachers can use other authentic sources.)
Reading Passage (Teachers can use other authentic sources.)
Grammar for IELTS: Unit 22 (The Passive)
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
UNIVERSITIES OF COMPUTER STUDIES
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.
University of Computer Studies
B.C.Sc. / B.C.Tech. (Fifth Year)
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).
University of Computer Studies
B.C.Sc. / B.C.Tech. (Fifth Year)
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
University of Computer Studies
B.C.Sc. / B.C.Tech. (Fifth Year)
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
2018-2019 Academic Year
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.
University of Computer Studies, Yangon
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
Chapter Review Questions
2 Assignment
Hands-On Activity (Two-factor authentication)
Critical Thinking Exercise
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
Chapter Review Questions
Hands-On Activity (OSSEC)
Critical Thinking Exercise
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
Chapter Review Questions
Critical Thinking Exercise
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
Chapter Review Questions
Critical Thinking Exercise
1 Assignment
Assignment
8. Chapter 13 - Policies, Standards and Guidelines 360-381 6
Guiding Principles
Key Policy Issues
4
1
Example Case - HB Gary
Chapter Review Questions
Critical Thinking Exercise
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
Semester hours 3 hours No. of Credit Units 3
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%
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
Semester hours 3 hours No. of Credit Units 3
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.
Reference Materials:
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.
2. Preparing for and participating in the recitations.
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
18 Practical + Tutorial 3
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
23 Practical + Tutorial 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
Semester hours 1 hours No. of Credit Units 1
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
2. Preparing for and participating in the recitations.
3. Assignments
4. Reading the text
5. Quiz
6. Exams
Reference Materials:
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
University of Computer Studies, Yangon
Faculty of Information Science
2018-2019 Academic Year
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%
Class Participation : 5%
University of Computer Studies
2017-2018 Academic Year
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
University of Computer Studies, Yangon
Faculty of Information Science
2018-2019 Academic Year
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%
Class Participation : 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
Chapter review/ Tutorial/Assessment 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
Chapter review/ Tutorial/Assessment 1
WebE Chapter 4: Communication 46
The Communication Activity 1
Formualtion 1
2
Elicitation 1
Identifying WebApp increments 1
Negotiation 1
Chapter review/ Tutorial/Assessment 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
Chapter review/ Tutorial/Assessment 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
Chapter review/ Tutorial/Assessment 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
Chapter review/ Tutorial/Assessment 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
Chapter review/ Tutorial/Assessment 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
Chapter review/ Tutorial/Assessment 1
Revision for all Chapters
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