Curriculum for M.Sc./Ph.D.

Curriculum for M.Sc./Ph.D. in Electrical Engineering

Two options for M.Sc. in Electrical Engineering program, each with total credit hours of 30, are being offered:

(a)           Thesis Option:                      8 Subjects (24 credit hours) + Research Thesis (6 credit hours)

(b)           Non-Thesis Option:            10 Subjects (30 credit hours) + Design Problem

Note: All courses are 3 (3+0) credit hours each unless otherwise specified.

Course Descriptions MS PhD EE 2021 Final.pdf

Computer
S. No. Course Code Course Title
1. EE-502 Stochastic Processes
2. EE-503 Linear Systems Theory
3. EE-506 Engineering Mathematics
4. EE-510 Advanced Computer Architecture
5. EE-511 Advanced Computer Networks
6. EE-512 Machine Learning
7. EE-516 Image and Video Processing
8. EE-517 Design and Analysis of Algorithms
9. EE-519 Cybersecurity
10. EE-527 Advanced VLSI System Design
11. EE-550 Deep Learning
12. EE-599a Special Topics in Computer
13. EE-611 Artificial Intelligence

 

Electronics & Communications
S. No. Course Code Course Title
1. EE-502 Stochastic Processes
2. EE-503 Linear Systems Theory
3. EE-506 Engineering Mathematics
4. EE-510 Advanced Computer Architecture
5. EE-511 Advanced Computer Networks
6. EE-516 Image and Video Processing
7. EE-520 Wireless and Mobile Communications
8. EE-521 Information and Coding Theory
9. EE-522 Statistical Signal Processing
10. EE-524 Optical Communications
11. EE-525 Advanced Electromagnetic Theory
12. EE-527 Advanced VLSI System Design
13. EE-528 Antenna Theory and Design
14. EE-529 Advanced Microwave Circuits
15. EE-561 Array Signal Processing
16. EE-562 Adaptive Array Processing
17. EE-563 Micro-Electro-Mechanical-Systems (MEMS)
18. EE-599b Special Topics in Electronics & Communications
19. EE-620 Advanced Wireless and Mobile Communications

 

Power Systems
S. No. Course Code Course Title
1. EE-502 Stochastic Processes
2. EE-503 Linear Systems Theory
3. EE-506 Engineering Mathematics
4. EE-530 Power Electronics Converters
5. EE-535 Control of Electric Machines Drives
6. EE-541 Power System Dynamics and Stability
7. EE-547 Advanced Power Electronics
8. EE-549 High Voltage DC and Flexible AC Transmission
9. EE-570 Power System Transients and Insulation Coordination
10. EE-571 Power Inverters
11. EE-572 Smart Grids and Renewable Energy Systems
12. EE-599d Special Topics in Power Systems
13. EE-641 Advanced Power System Operation and Control
14. EE-642 Condition Monitoring of High Voltage Equipment
15. EE-643 Power System Reliability

 

SUBJECT OFFERED IN M.SC. TELECOMMUNICATION NETWORKS

Two options for M.Sc. in Telecommunication Networks program, each with total credit hours of 30, are being offered:

(a)           Thesis Option:                      8 Subjects (24 credit hours) + Research Thesis (6 credit hours)

(b)           Non-Thesis Option:            10 Subjects (30 credit hours) + Design Problem

Note: All courses are 3 (3+0) credit hours each unless otherwise specified.

Revised Course Description MS TN.pdf

Semester-I (Group-A)
TN 500 – Mathematics for Networks
TN 520 – Advanced Communication Systems
TN 530 – Network Programming
Semester-II (Group-A)
TN 531 – Software Defined Networking
TN 522 – Optical Networks
TN 533 – Network Security and Cryptography
Semesters-III, IV (Group-A & B)
TN 502 – Optimization Theory
TN 550 – Queuing Theory
TN 561 – Next Generation Networks (3+1)
TN 562 – Broadband Access Network (3+1)
TN 564 – Radio Frequency Engineering (3+1)

 

SUBJECT OFFERED IN M.SC. ARTIFICIAL INTELLIGENCE

The curriculum for the M.Sc. in AI requires three Core courses, five Electives, and a Thesis (or two further Electives):  Elective courses are divided into two specializations. Students will have to choose at least one course from each specialization. The specializations are:

  1. Applications of Artificial Intelligence
  2. Theory of Artificial Intelligence

Note: All courses are 3 (3+0) credit hours each unless otherwise specified.

Course Description MS AI 2021.pdf

M.Sc. in Artificial Intelligence
Core Courses
AI-501: Mathematical and Computational Foundations for Artificial Intelligence
AI-502: Artificial Intelligence
AI-503: Machine Learning
Applications of Artificial Intelligence
AI-511: Deep Neural Networks
AI-512: Natural Language Processing
AI-513: Computer Vision
AI-514: Reinforcement Learning
AI-515: Modern Robotics
AI-516: Artificial Intelligence for Robotics
Theory of Artificial Intelligence
AI-521: Statistical Learning Theory
AI-522: Advanced Machine Learning
AI-523: Convex Optimization
AI-524: Probabilistic Graphical Models
AI-525: Special Topics in Machine Learning
AI-526: Intelligent Control Systems
AI-527: Aspects of Computational Intelligence