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MS in Electrical, Electronics & Communication Engineering

Course Work 

On joining the Institute every student is required to plan his/her coursework in consultation with a Faculty Advisor.

 Credit Requirements 

  1. All students of the MS programme are normally required to complete the prescribed 34 credits within the first two semesters from the date of joining, by completing the coursework prescribed by the faculty advisor. 

  2. In addition, the research scholars should complete PP/NP course on Communication Skills.

  3. MS students will be allowed to take additional courses beyond the prescribed 34 credits, with the approval of APEC.

  4. MS students will be allowed to take only one UG course for credit requirements. 

Additional details on course work can be found in MS-R.4.1 in MS Rule Book 

Performance Requirements 

 A MS student must fulfil the following performance requirements: 

A student MUST get at least CC grade in EVERY course (other than optional) registered as a credit course, including seminar.

Academic Probation to the students having lower grade than the minimum requirement for continuation of their studies may be given. For students who have scored grade lower than CC in at most one course in their first semester may be offered an academic probation, with appropriate conditions decided by APEC.

Syllabus for MS in EECE

List of PG Courses from EECE Department

S.No

Course Code

Name of Course

L-T-P-C

1EE 601Analog IC Design3-0-0-6
2EE 602Probability Models3-0-0-3
3EE 603Electric Drives for EVs - I3-0-0-3
4EE 604Electric Drives for EVs - II3-0-0-3
5EE 605Probability Theory and Random Processes3-0-1-6
6EE 606Pattern Recognition and Machine Learning3-0-0-6
7EE 607Power System Dynamics and Control2-0-1-6
8EE 608Wireless Communication3-0-0-6
9EE 609Pattern Recognition and Machine Learning3-0-3-9
10EE 610VLSI Design3-0-0-6
11EE 611Neural networks and deep learning (NNDL) Laboratory0-0-3-3
12EE 612Pattern Recognition and Machine Learning (PRML) Laboratory0-0-3-3
13EE 613Speech Processing Laboratory0-0-3-3
14EE 614Data Analysis and Visualization Lab0-0-3-3
15EE 620Neural Networks and Deep Learning3-0-0-6
16EE 621Speech Processing3-0-0-6
17EE 622Multivariable Control Systems3-0-0-6
18EE 623Advanced Power Electronics and Drives3-0-0-6
19EE 624Optimization Theory and Algorithms3-0-0-6
20EE 625Design of Power Converters2-0-1-6
21EE 626VLSI Technology3-0-0-6
22EE 627Advanced Power Systems3-0-0-6
23EE 628Modeling and Control of Renewable Energy Resources3-0-0-6
24EE 629Probability Models and Applications (PMA)3-0-0-6
25EE 630Advanced Topics in Speech Processing3-0-0-6
26EE 631Advanced Electric drives2-0-2-6 
27EE 632System design of electronic products3-0-0-6
28EE 633Mixed signal VLSI Design3-0-0-6
29EE 634Linear Algebra and its Applications3-0-0-6
30EE 635Speech Processing3-0-3-9
31EE 636Advanced Analog Circuits3-0-0-6
32EE 637Physics of Nanoscale Devices3-0-0-6
33EE 638Advanced Topics in Control Systems3-0-0-6
34EE 639Modern Statistics for Engineers3-0-0-6
35EE 640Game Theory with Control Applications3-0-0-6
36EE 641Renewable Energy3-0-0-6
37EE 642Microgrid Dynamics and Control3-0-0-6
38EE 643Power System Operation and Control3-0-0-6
39EE 644Power System II3-0-0-6
40EE 645Electrical Machines II3-0-0-6
41EE 646Advanced Topics in Artificial Intelligence3-0-0-6
42EE 647Introduction to Machine Learning3-0-0-6
43EE 648Nanoelectronics3-0-0-6
44EE 650Introduction to Aerial Robots2-1-0-6
45EE 651Dynamics and control of aerial robots2-1-0-6
46EE 652    Autonomous navigation2-1-0-6
47EE 653    Electric Vehicles: Systems and Components3-0-0-6
48EE 654Smart Grid3-0-0-6
49EE 655Data Science and Visualization Lab0-0-3-3
50EE 656    VLSI Testing and Testability3-0-0-6
51EE 657Introduction to HIL testing methods1-0-1-3
52EE 658Battery Technology3-0-0-6
53EE 659Electric Vehicles: Systems and Components2-0-2-6
54EE 660Introduction to Electric Drives3-0-0-6
55EE 661    EV Charging and Ancillary Services3-0-0-6
56EE 662Advanced Methods in HIL Testing of Electric Transportation Systems2-0-2-6
57EE 664Electric and Hybrid Vehicles3-0-0-6
58EE 665    Robotics and Automation3-0-2-8
59EE 666    Intro to EV Architecture1.5-0-3-3
60EE 667Stochastic Process and its Applications3-0-0-3
61EE 668Mathematics for Data Science I3-0-0-3
62EE 669Mathematics for Data Science II3-0-0-3
63EE 670Fundamentals of Speech Processing (FSP)3-0-0-3
64EE 671Machine Learning of Speech Processing (MLSP)1.5-0-0-3
65EE 672    Deep Learning of Speech Processing (DLSP)1.5-0-0-3
66EE 673Pattern Recognition3-0-0-3
67EE 693Machine Learning (Ml)1.5-0-0-3
68EE 675Artificial Neural Networks (Ann)3-0-0-3
69EE 676Deep Learning (Dl)1.5-0-0-3
70EE 677Introduction to Battery Management Systems3-0-0-3
71EE 678PWM Techniques3-0-0-3
72EE 679Signals, Systems and Controls3-0-0-3
73EE 680Digital Signal Processing and Communications3-0-0-3
74EE 681Machine Learning (Ml)1.5-0-3-3
75EE 682Computational Techniques And Optimisation1.5-0-3-3
76EE 683Embedded Systems1.5-0-3-3
77EE 684Design of Power Converters1.5-0-3-3
78EE 687Optimization Methods for Wireless Communication and Machine Learning3-0-0-6
79EE 688Physics of Transistor3-0-0-6
80EE 689Semiconductor Radiation Detectors3-0-0-6
81EE 701Power Semiconductor Devices3-0-0-6
82EE 706Advanced Topics in Signal Processing3-0-0-6
83EE 703    Stochastic Control and Learning for Networked systems3-0-0-6
84EE 704Theory of Machine Learning3-0-0-6
85EE 705Seminar