Major Areas:
The Master of Science in Computer Science and Engineering (MS in CSE) program is organized into four major areas:
Data Science
Software Engineering
Networking
Systems Engineering
A student will have to declare her/his major area during enrollment into the program. However, a student can change her/his major area before the start of the second semester with the permission of the Chairperson of the Department.
Admission Requirements:
Candidates must have a 4-year Bachelor degree in any of the following disciplines with a minimum CGPA of 2.5 on a 4.0 point scale or equivalent standing from any recognized institution:
- Computer Science and Engineering/ Computer Science/ Computer Engineering/ Software Engineering/ Information and Communications Engineering/ Equivalent
- Any engineering discipline with calculus, statistics, and programming (pre-requisite course(s) needed)
- Any physical/mathematical/biological science discipline with calculus, statistics, and programming (pre-requisite course(s) needed)
Candidates must have passed HSC/Equivalent program from Science group.
Candidates must pass an admission test administered by the university.
Study Track:
A student can pursue the MS in CSE program in either of two tracks:
Thesis Track
Project Track
A student will have to declare her/
his study track during enrollment into the program. However, a student can change her/his study track during the progress of the program with the permission of the Chairperson of the Department.
Length of the Program:
The length of the MS in CSE program is 3 semesters (one year). However, students may take up to 15 semester (five years) for completing the degree.
MS in CSE Program Cost:
|
Name of Programs
|
Total Credit
|
Fee per Credit
|
Tuition Fees
|
Lab & Activities Fees
|
Admission Fee
|
Grand Total
|
|---|---|---|---|---|---|---|
|
MS in CSE
|
40.0
|
4,500/=
|
180,000/=
|
9,000/=
|
20,000/=
|
209,000/=
|
Degree Requirement:
A candidate for the degree of MS in CSE must complete at least 33 credits with a minimum CGPA of 2.5 on a 4.0 point scale. The course requirements will be as follows.
Thesis Track
| Course Category | Number of Courses | Credit |
|---|---|---|
| Compulsory Courses for all Major Areas | 3 | 9 |
| Compulsory Courses from Major Area | 2 | 6 |
| Elective Courses from Major Area | 3 | 9 |
| Master Thesis |
|
9 |
| Total | 33 |
Project Track
| Course Category | Number of Courses | Credit |
|---|---|---|
| Compulsory Courses for all Major Areas | 3 | 9 |
| Compulsory Courses from Major Area | 2 | 6 |
| Elective Courses from Major Area | 5 | 15 |
| Master Project |
|
3 |
| Total | 33 |
Course Summary:
| Compulsory Courses for all Major Areas | Credit | Comment |
|---|---|---|
| Non-credit pre-requisite (if not done in the Bachelor program) |
|
|
| CSE503 Data Structures | 3 | Pass or Fail |
| Compulsory Courses |
|
|
| CSE504 Algorithms | 3 |
|
| CSE505 Database Systems | 3 |
|
| CSE596 IT Project Management and Entrepreneurship | 3 |
|
| 1. Major Area: Data Science | Credit | Comment |
|---|---|---|
| Non-credit pre-requisite (if not done in the Bachelor program) | ||
| CSE506 Artificial Intelligence | 3 | Pass or Fail |
| Compulsory Courses | ||
| CSE520 Statistics for Data Science | 3 |
|
| CSE521 Machine Learning | 3 |
|
| Elective Courses | ||
| CSE522 Data Mining | 3 |
|
| CSE523 Digital Image Processing | 3 |
|
| CSE524 Computer Vision | 3 |
|
| CSE525 Pattern Recognition | 3 |
|
| CSE526 Bioinformatics Algorithms | 3 |
|
| CSE527 Big Data Analytics | 3 |
|
| CSE560 Distributed Systems and Algorithms | 3 |
|
| 2. Major Area: Software Engineering | Credit | Comment |
|---|---|---|
| Non-credit pre-requisite (if not done in the Bachelor program) | ||
| CSE507 Information System Analysis and Design | 3 | Pass or Fail |
| Compulsory Courses | ||
| CSE550 Software Engineering | 3 |
|
| CSE551 Software Testing and Quality Assurance | 3 |
|
| Elective Courses | ||
| CSE552 Simulation and Modeling | 3 |
|
| CSE553 Software Architecture | 3 |
|
| CSE554 Human Computer Interactions | 3 |
|
| CSE555 Advanced Database System | 3 |
|
| CSE556 Web Programming | 3 |
|
| CSE560 Distributed Systems and Algorithms | 3 |
|
| CSE565 Mobile Programming | 3 |
|
| CSE521 Machine Learning | 3 |
|
| CSE522 Data Mining | 3 |
|
| CSE526 Bioinformatics Algorithms | 3 |
|
| CSE527 Big Data Analytics | 3 |
|
| 3. Major Area: Networking | Credit | Comment |
|---|---|---|
| Non-credit pre-requisite (if not done in the Bachelor program) | ||
| CSE508 Computer Networks | 3 | Pass or Fail |
| Compulsory Courses | ||
| CSE560 Distributed Systems and Algorithms | 3 |
|
| CSE561 Advanced Network Services and Management | 3 |
|
| Elective Courses | ||
| CSE562 Wireless Networks | 3 |
|
| CSE563 Cellular Networks | 3 |
|
| CSE564 Network Security and Systems | 3 |
|
| CSE565 Mobile Programming | 3 |
|
| CSE521 Machine Learning | 3 |
|
| CSE522 Data Mining | 3 |
|
| CSE527 Big Data Analytics | 3 |
|
| 4. Major Area: Systems Engineering | Credit | Comment |
|---|---|---|
| Non-credit pre-requisite (if not done in the Bachelor program) | ||
| CSE509 Digital Logic Design | 3 | Pass or Fail |
| Compulsory Courses | ||
| CSE5570 Internet of Things | 3 |
|
| CSE571 Microprocessors and Microcontrollers | 3 |
|
| Elective Courses | ||
| CSE572 ASIC Design Using FPGA | 3 |
|
| CSE573 VLSI Design | 3 |
|
| CSE574 Robotics | 3 |
|
| CSE575 Embedded Systems | 3 |
|
| CSE521 Machine Learning | 3 |
|
| CSE522 Data Mining | 3 |
|
| CSE527 Big Data Analytics | 3 |
|
| CSE560 Distributed Systems and Algorithms | 3 |
|
| CSE561 Advanced Network Services and Management | 3 |
|
| Thesis/Project | Credit | Comment |
|---|---|---|
| CSE597 Master Project | 3 |
|
| CSE599 Master Thesis | 9 |
|
Program Overview
The M.Sc. in Artificial Intelligence and Machine Learning is designed for students who want to develop deep knowledge and advanced skills in AI, machine learning, data engineering, analytics, natural language processing, computer vision, robotics, and related technologies. The program combines theoretical foundations with strong practical and research orientation.
Students will engage in coursework, lab activities, project work, thesis research, seminars, workshops, and industry interactions to prepare for careers in industry, research, entrepreneurship, and academia.
Key Features
- Strong foundation in AI, ML, data engineering, analytics, and computational methods
- Exposure to frontier areas such as quantum computing, blockchain, advanced robotics, and large-scale data mining
- Specialized AI and ML lab-based learning
- Thesis and project opportunities on real-world challenges
- Interdisciplinary emphasis on ethics, policy, business, and societal impact
- Technical writing, oral presentation, teamwork, and leadership development
- Alumni, industry, and expert engagement through seminars and mentoring
Admission Requirements
Candidates must have:
- A 4-year bachelor’s degree in any discipline with calculus, statistics, and programming
- A minimum CGPA of 2.5 on a 4.0 scale or equivalent
- A passing score in the admission test administered by the university
Prerequisite Requirement for Non-CSE Candidates
Applicants from non-CSE backgrounds must have prior undergraduate coursework in:
- Calculus (minimum 3 credits)
- Statistics/Probability (minimum 3 credits)
- Programming (minimum 3 credits)
Only formal undergraduate coursework will be accepted for prerequisite fulfillment
Program Structure
- Duration: Minimum 1.5 years, maximum 5 years
- Total Credits: 40
- Minimum CGPA for Graduation: 2.5
Thesis Track
- Coursework: 30 credits
- Master Thesis: 10 credits
Project Track
- Coursework: 36 credits
- Master Project: 4 credits
Credit Summary
|
Course Category |
Thesis Track |
Project Track |
|
General Education Courses |
6 |
6 |
|
Compulsory Courses |
18 |
18 |
|
Elective Courses |
6 |
12 |
|
Master Thesis / Project |
10 |
4 |
|
Grand Total |
40 |
40 |
Course Listings:
General Education Courses
- (Interdisciplinary courses, beyond the discipline/program, that provides a well-rounded learning experience to the students of an academic program)
|
|
|
|
||
|
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|
|
|
|
|
|
|
|
Core courses (Courses that characterize discipline)
|
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||
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Elective Courses (Courses for specialization within the discipline)
The elective courses are grouped into several application-focused subdomains that allow students to specialize in areas such as Data & Knowledge Engineering, Intelligent Systems & Robotics, Advanced Machine Learning, and Natural Language Processing. Each subdomain helps students align their elective choices with specific career paths and industry application areas.
|
Specialization |
Course Code |
Course Title |
Credits |
|
Advanced Machine Learning |
AIML512 |
Deep Learning |
3.0 |
|
AIML531 |
Pattern Recognition |
3.0 |
|
|
AIML525 |
Computational Intelligence |
3.0 |
|
|
AIML520 |
Computer Vision |
3.0 |
|
|
AIML517 |
Digital Image Processing |
3.0 |
|
|
Natural Language Processing (NLP) |
AIML517 |
Natural Language Processing |
3.0 |
|
Intelligent Systems & Robotics |
AIML513 |
Robotics |
3.0 |
|
AIML519 |
AI for Industrial Automation |
3.0 |
|
|
AIML529 |
Autonomous Systems |
3.0 |
|
|
AIML527 |
Expert Systems |
3.0 |
|
|
AIML530 |
Digital Signal Processing |
3.0 |
|
|
Data & Knowledge Engineering |
AIML509 |
Cloud Computing |
3.0 |
|
AIML514 |
Big Data Analysis and Visualization |
3.0 |
|
|
AIML515 |
Business Intelligence |
3.0 |
|
|
AIML526 |
Data Mining |
3.0 |
|
|
AIML528 |
Data and Knowledge Engineering |
3.0 |
|
|
Application Domains |
AIML522 |
AI in Healthcare |
3.0 |
|
AIML523 |
AI in Agriculture |
3.0 |
|
|
AIML521 |
AI in Transportation |
3.0 |
|
|
AIML524 |
Cyber Security |
3.0 |
|
|
AIML510 |
Internet of Things (IoT) |
3.0 |
|
|
AIML518 |
Game Development |
3.0 |
|
|
|
AIML511 |
Software Development for AI and ML |
3.0 |
Thesis/Projects/Portfolio (as applicable for the discipline/ academic program)
|
|
|
|
|
|
|
|
|
|
PEO |
Statement |
|
PEO 1 |
Graduates will establish themselves as leading computational professionals and/or entrepreneurs and continue to learn and address evolving challenges in AI and ML. |
|
PEO 2 |
Graduates will engage in lifelong learning and interdisciplinary development for industrial, research, academic, leadership, and entrepreneurial careers. |
|
PEO 3 |
Graduates will contribute to sustainable development and social well-being through ethical and responsible use of AI and ML principles, practices, and tools. |
Program Learning Outcomes (PLOs)
|
PLO |
Description |
|
PLO 1 |
Apply knowledge of mathematics, statistics, natural sciences, and specialized disciplinary knowledge to solve complex problems. |
|
PLO 2 |
Identify, formulate, research, and analyze complex problems and reach substantiated conclusions. |
|
PLO 3 |
Design solutions considering safety, legal, ethical, cultural, societal, environmental, and sustainability issues. |
|
PLO 4 |
Conduct investigation using research-based knowledge, experiments, analysis, and modern tools. |
|
PLO 5 |
Function effectively as a member or leader in teams and communicate effectively with professionals and society. |
|
PLO 6 |
Engage in independent and lifelong learning in response to technological change. |
M.Sc. in Artificial Intelligence and Machine Learning Cost
|
Name of Programs
|
Total Credit
|
Fee per Credit
|
Tuition Fees
|
Lab & Activities Fees
|
Admission Fee
|
Grand Total
|
|---|---|---|---|---|---|---|
|
M.Sc. in Artificial Intelligence
|
40.0
|
4,500/=
|
180,000/=
|
9,000/=
|
20,000/=
|
209,000/=
|
Admission Contact
|
Item |
Details |
|
Department |
Department of Computer Science and Engineering |
|
University |
East West University |
|
Address |
A/2 Jahurul Islam Avenue, Aftabnagar, Dhaka-1212 |
|
Phone |
09666775577 |
|
|
|
|
Website |
admission.ewubd.edu |
|
Application Deadline |
April 30, 2026 |
Apply for Summer 2026
Join East West University’s new graduate programs in Artificial Intelligence and Machine Learning and Cyber Security. Build advanced skills, explore research opportunities, and prepare for leadership in fast-growing technology fields.
Program Overview
The M.Sc. in Cyber Security is designed to develop advanced professionals capable of protecting digital infrastructures, managing cyber risks, conducting forensic investigations, and designing secure computing environments. The program combines theoretical grounding with strong applied learning in network security, cryptography, digital forensics, ethical hacking, blockchain security, machine learning for cybersecurity, governance, privacy, cybercrime, and cloud security.
Key Features
- Industry-aligned cybersecurity curriculum
- Strong focus on network security, digital forensics, ethical hacking, and blockchain
- Exposure to emerging areas such as quantum-resistant cryptography, threat intelligence, cloud and edge security, cyber-physical security, and security analytics
- Specialized lab and hands-on training opportunities
- Strong integration of legal, policy, ethical, business, and governance dimensions
- Case analysis, incident response exercises, technical reporting, and oral presentations
- Career-focused learning for industry, research, and leadership roles
Admission Requirements
Candidates must have:
- A 4-year bachelor’s degree in any discipline with calculus, statistics, and programming
- A minimum CGPA of 2.5 on a 4.0 scale or equivalent
- A passing score in the admission test administered by the university
Prerequisite Requirement for Non-CSE Candidates
Applicants from non-CSE backgrounds must have prior undergraduate coursework in:
- Calculus (minimum 3 credits)
- Statistics/Probability (minimum 3 credits)
- Programming (minimum 3 credits)
Only formal undergraduate coursework will be accepted for prerequisite fulfillment.
Program Structure
- Duration: Minimum 1.5 years, maximum 5 years
- Total Credits: 40
- Minimum CGPA for Graduation: 2.5
Thesis Track
- Coursework: 30 credits
- Master Thesis: 10 credits
Project Track
- Coursework: 36 credits
- Master Project: 4 credits
Credit Summary
|
Course Category |
Thesis Track |
Project Track |
|
General Education Courses |
6 |
6 |
|
Compulsory Courses |
18 |
18 |
|
Elective Courses |
6 |
12 |
|
Master Thesis / Project |
10 |
4 |
|
Grand Total |
40 |
40 |
Course Listings:
General Education Courses
- (Interdisciplinary courses, beyond the discipline/program, that provides a well-rounded learning experience to the students of an academic program)
|
|
|
|
||
|
|
|
|
|
|
|
|
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|
|
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|
||
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Specialization |
Course Code |
Course Title |
Credits |
|
Network & Infrastructure Security |
CyS514 |
Secure Network Architecture |
3.0 |
|
CyS521 |
Network Security Monitoring |
3.0 |
|
|
CyS523 |
Network Defense and Countermeasures |
3.0 |
|
|
Cryptography, Blockchain & Distributed Systems Security |
CyS510 |
Cryptography |
3.0 |
|
CyS515 |
Block Chain and Crypto Currency |
3.0 |
|
|
CyS525 |
Decentralized Finance and Blockchain |
3.0 |
|
|
CyS526 |
Advanced Smart Contracts and DApp Development |
3.0 |
|
|
Secure Software Engineering & Application Security |
CyS509 |
Web Service Security |
3.0 |
|
CyS516 |
Secure Software Development and Testing |
3.0 |
|
|
CyS513 |
Cybersecurity Analytics |
3.0 |
|
|
Cloud, Virtualization & Emerging Technologies Security |
CyS511 |
Cloud Security and Virtualization |
3.0 |
|
CyS517 |
Internet of Things Security |
3.0 |
|
|
CyS518 |
Intelligent Systems for Cyber Security |
3.0 |
|
|
Governance, Risk, Compliance & Cyber Policy |
CyS512 |
Data Privacy and Laws |
3.0 |
|
CyS520 |
Legal and Ethical Issues in Cyber Security |
3.0 |
|
|
CyS522 |
Cybersecurity Audit and Assessment Strategies |
3.0 |
|
|
CyS524 |
Business and Finance Cyber Security |
3.0 |
|
|
Cybercrime, Threats & National/Organizational Security |
CyS519 |
Cyber Crimes and Terrorism |
3.0 |
|
CyS527 |
Supply Chain Cyber Security |
3.0 |
Thesis/Projects/Portfolio (as applicable for the discipline/ academic program)
|
|
|
|
|
|
|
|
|
Program Educational Objectives (PEOs)
|
PEO |
Statement |
|
PEO 1 |
Graduates will establish themselves as leading professionals and/or entrepreneurs and continue to learn and address evolving challenges in Cyber Security. |
|
PEO 2 |
Graduates will engage in lifelong pursuit of knowledge and interdisciplinary learning for industrial, research, academic, leadership, and entrepreneurial careers. |
|
PEO 3 |
Graduates will contribute to sustainable development and social well-being through ethical and responsible use of cybersecurity principles, practices, and tools. |
Program Learning Outcomes (PLOs)
|
PLO |
Description |
|
PLO 1 |
Apply knowledge of mathematics, statistics, natural sciences, and specialized knowledge to solve complex problems. |
|
PLO 2 |
Identify, formulate, research, and analyze complex problems and reach substantiated conclusions. |
|
PLO 3 |
Design solutions considering public safety, legal, ethical, cultural, societal, environmental, and sustainability issues. |
|
PLO 4 |
Conduct investigations using research-based knowledge, methods, experiments, and modern tools. |
|
PLO 5 |
Function effectively as a member or leader in teams and communicate effectively with professional communities and society. |
|
PLO 6 |
Engage in independent and lifelong learning in the context of technological change. |
M.Sc. in Cyber Security Cost
|
Name of Programs
|
Total Credit
|
Fee per Credit
|
Tuition Fees
|
Lab & Activities Fees
|
Admission Fee
|
Grand Total
|
|---|---|---|---|---|---|---|
| M.Sc. in Cyber Security
|
40.0 | 4,500/= | 180,000/= | 9,000/= | 20,000/= | 209,000/=
|
Admission Contact
|
Item |
Details |
|
Department |
Department of Computer Science and Engineering |
|
University |
East West University |
|
Address |
A/2 Jahurul Islam Avenue, Aftabnagar, Dhaka-1212 |
|
Phone |
09666775577 |
|
|
|
|
Website |
admission.ewubd.edu |
|
Application Deadline |
April 30, 2026 |
Apply for Summer 2026
Join East West University’s new graduate programs in Artificial Intelligence and Machine Learning and Cyber Security. Build advanced skills, explore research opportunities, and prepare for leadership in fast-growing technology fields.
