School of Electrical & Electronics Engineering


Artificial Intelligence and Robotics

Program Outcomes (POs):

Graduates Attributes (GAs) form a set of individually assessable outcomes that are the components indicative of the graduate’s potential to acquire competence to practice at the appropriate level. The GAs of PG programmes are examples of the attributes expected from a graduate of an accredited programme. The Graduate Attributes of PG programmes of the NBA are as following: 

  1. Scholarship of Knowledge:
    Acquire in-depth knowledge of specific discipline or professional area, including wider and global perspective, with an ability to discriminate, evaluate, analyse, and synthesise existing and new knowledge, and integration of the same for enhancement of knowledge. 
  2. Critical Thinking
    Analyse complex engineering problems critically, apply independent judgement for synthesising information to make intellectual and/or creative advances for conducting research in a wider theoretical, practical and policy context. 
  3. Problem Solving
    Think laterally and originally, conceptualise, and solve engineering problems, evaluate a wide range of potential solutions for those problems and arrive at feasible, optimal solutions after considering public health and safety, cultural, societal and environmental factors in the core areas of expertise. 
  4. Research Skill
    Extract information pertinent to unfamiliar problems through literature survey and experiments, apply appropriate research methodologies, techniques and tools, design, conduct experiments, analyse and interpret data, demonstrate higher order skill and view things in a broader perspective, contribute individually/in group(s) to the development of scientific/technological knowledge in one or more domains of engineering. 
  5. Usage of modern tools
    Create, select, learn, and apply appropriate techniques, resources, and modern engineering and IT tools, including prediction and modelling, to complex engineering activities with an understanding of the limitations. 
  6. Collaborative and Multidisciplinary work
    Possess knowledge and understanding of group dynamics, recognise opportunities and contribute positively to collaborative-multidisciplinary scientific research, demonstrate a capacity for self-management and teamwork, decision-making based on open-mindedness, objectivity, and rational analysis in order to achieve common goals and further the learning of themselves as well as others. 
  7. Project Management and Finance
    Demonstrate knowledge and understanding of engineering and management principles and apply the same to one’s own work, as a member and leader in a team, manage projects efficiently in respective disciplines and multidisciplinary environments after consideration of economic and financial factors. 
  8. Communication
    Communicate with the engineering community, and with society at large, regarding complex engineering activities confidently and effectively, such as, being able to comprehend and write effective reports and design documentation by adhering to appropriate standards, make effective presentations, and give and receive clear instructions. 
  9. Life-long Learning
    Recognise the need for and have the preparation and ability to engage in life-long learning independently, with a high level of enthusiasm and commitment to improve knowledge and competence continuously. 
  10. Ethical Practices and Social Responsibility
    Acquire professional and intellectual integrity, professional code of conduct, ethics of research and scholarship, consideration of the impact of research outcomes on professional practices and an understanding of responsibility to contribute to the community for sustainable development of society. 
  11. Independent and Reflective Learning
    Observe and examine critically the outcomes of one’s actions and make corrective measures subsequently and learn from mistakes without depending on external feedback.

Program Specific Outcomes (PSOs):

The framework of M.Tech. in Artificial Intelligence & Robotics at SASTRA Deemed University is oriented towards imparting core competence in the field of AI and Robotics. Upon completion of this programme, graduates will be able to:

  • Model the kinematics and dynamics of robots, which can be used to predict its behaviour and performance using computational tools
  • Design and develop complete robotics solutions for industrial and societal problems
  • Troubleshoot the software and hardware systems of robots in a systematic mode firmly rooted in the knowledge and understanding of fundamental engineering principles
  • Work on the development of environment specific robotic systems like aerial, underwater, and surface robots in single and multi-robot configurations
  • Demonstrate the ability to apply basic research methods in control, perception, and navigation aspects of robots, including data analysis and interpretation
  • Implement robotic systems that integrate microprocessors / onboard computers, electro-mechanical systems, and machine learning algorithms
  • Integrate artificial intelligence with robots and other systems to improve their performance in challenging environmental conditions and for solving contemporary problems prevalent in society
  • Design, debug, test, and document a robotic automation system for industrial applications
  • Acquire new knowledge and integrate knowledge from different disciplines through critical reading of research material 


Scheme of Study

(Students admitted from 2020-21)

I Semester (22 Credits)

Course Code Course Name No of Contact Hours / Week Credits
MCT501 Kinematics and Dynamics of Robots 3 0 2 4
MAT445 Probability and Statistics using R 3 0 2 4
BIN522 Python for Data Science 2 1 2 4
INT530 Artificial Intelligence and Reasoning 3 0 2 4
EIE501 Advanced Control Systems 4 0 0 4
EIE502 Modelling and Simulation Lab 0 0 2 1
TNP101 Soft Skills-I 0 0 2 1
TOTAL 15 1 12 22

II Semester (25 Credits)

Course Code Course Name No of Contact Hours / Week Credits
EIE608 Autonomous Navigation 2 0 4 4
INT532 Machine Learning 4 0 2 5
XXXXXX Department Elective – I X X X 4
XXXXXX Department Elective – II X X X 4
MAN106 Research Methodology & IPR 2 0 0 2
OEXXXX Open Elective – I(Ethics) 3 0 0 3
EIE507 Robot Design and Programming Lab 0 0 2 1
TNP102 Soft Skills-II 0 0 2 1
EIE505 Seminar 0 0 2 1
TOTAL 17-19 0-2 12-16 25

Summer Project (3 Credits)

Course Code Course Name Hours / Week Credits
EIE506 Summer Project 0 0 6 3
TOTAL 0 0 6 3

III Semester (25 Credits)

Course Code Course Name No of Contact Hours / Week Credits
CSE420 Computer Vision 3 1 0 4
CSE423 Deep Learning 4 0 0 4
XXXXXX Department Elective – III X X X 4
XXXXXX Department Elective – IV X X X 4
OEXXXX Open Elective – II 3 0 0 3
CSE424 Deep Learning Laboratory 0 0 2 1
MAN107 Digital Pedagogy & Collaborative Learning 2 0 0 2
EIE610 Project Phase-I 0 0 6 3
TOTAL 17-19 2-4 8-12 25

IV Semester (15 Credits)

Course Code Course Name No of Contact Hours / Week Credits
EIE613 Project Work & Viva Voce 0 0 30 15
TOTAL 0 0 30 15

List of Electives – Proposed for II Semester (Any two to be selected)

Course Code Course Name
ECE512 Digital Speech Processing
EIE508 Advanced Biomechanics
MCT502 Mechatronic system design
EIE601 Optimal Control Systems
EIE602 Multisensor Fusion Techniques
EIE603 Detection and Estimation Theory
EIE605 Multi Robot Systems
EIE503 Underwater and Aerial Vehicles
MAT547 Game Theory & Applications
INT410 Cyber Physical Systems

List of Electives – Proposed - for III Semester (Any Two to be selected)

Course Code Course Name
EIE611 Underactuated Robotics
EIE612 Reinforcement Learning and Control
EIE509 Micro Electromechanical Systems
MCT602 Path Planning & Navigation Algorithms
CSE405 Natural Language Processing
EIE606 Applied Nonlinear Control
EIE604 Advanced Network Control Systems
ECE523 Industrial IOT
ICT101 Data Structures and Algorithms