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M-TECH in Intelligent System Robotics at Jadavpur University
Kolkata, West Bengal
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About the Specialization
What is Intelligent System & Robotics at Jadavpur University Kolkata?
This M.Tech Intelligent Systems program at Jadavpur University provides a robust foundation in Artificial Intelligence, Machine Learning, and Robotics. It prepares students for a dynamic career in India''''s rapidly expanding deep tech sector, focusing on both theoretical depth and practical application in creating intelligent autonomous systems for diverse industries.
Who Should Apply?
This program is ideal for engineering graduates with a B.E./B.Tech. in Computer Science, IT, Electronics, or Electrical Engineering, possessing a valid GATE score and a keen interest in advanced AI/ML/Robotics. It also suits working professionals aiming to specialize and enhance their skills for R&D, product development, or academic roles in intelligent automation.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding careers as AI Engineers, Robotics Specialists, Machine Learning Scientists, Data Scientists, or Research Associates within leading Indian and multinational companies. Entry-level salaries typically range from INR 6-12 LPA, with significant growth trajectories in areas like intelligent automation, smart manufacturing, and advanced analytics.
Student Success Practices
Foundation Stage
Master Core AI/ML/DS Fundamentals- (Semester 1-2)
Dedicate early semesters to building an unshakeable understanding of Artificial Intelligence, Machine Learning, Data Structures, and Algorithms. Utilize online courses from NPTEL, Coursera, or edX for supplementary learning, and consistently practice coding on platforms like HackerRank or LeetCode to solidify problem-solving skills, which are crucial for technical interviews.
Tools & Resources
NPTEL courses, Coursera, edX, HackerRank, LeetCode, Textbooks on AI/ML/DS
Career Connection
A strong foundation ensures academic excellence, forms the bedrock for advanced topics, and is frequently tested in technical rounds during campus placements, making you a strong candidate for entry-level roles.
Maximize Hands-on Lab and Project Work- (Semester 1-2)
Actively engage in all lab sessions, exploring tools like Python (with libraries like scikit-learn, TensorFlow, PyTorch), Prolog, and robot simulation software. Proactively initiate small projects to apply theoretical knowledge to practical problems. Document all code and projects on GitHub.
Tools & Resources
Python, scikit-learn, TensorFlow, PyTorch, Prolog, Robot Simulation Software, GitHub
Career Connection
Practical experience is highly valued by recruiters. Showcasing demonstrable projects on platforms like GitHub significantly enhances your resume and provides talking points in interviews for roles in product development and R&D.
Join Peer Learning & Academic Support Groups- (Semester 1-2)
Form study groups with classmates to discuss complex concepts, solve assignments collaboratively, and prepare for internal and external examinations. Peer teaching helps reinforce understanding and exposes you to different problem-solving approaches. Utilize university academic support services if available.
Tools & Resources
Study groups, University library resources, Faculty office hours
Career Connection
Collaborative learning improves overall academic performance, develops teamwork skills, and helps in creating a supportive network which can be beneficial for future professional collaborations and referrals.
Intermediate Stage
Strategic Elective Specialization- (Semester 3)
Carefully choose elective subjects that align with your specific career interests (e.g., Natural Language Processing, Computer Vision, Robotics, Data Mining). Dive deeper into these chosen areas through advanced readings, research papers, and specialized online courses. This allows for focused skill development.
Tools & Resources
Specialized textbooks, Research papers (IEEE, ACM), MOOCs for advanced topics
Career Connection
Specialized knowledge makes you a highly targeted candidate for specific roles in companies focusing on particular AI/Robotics domains, differentiating you from generalists and potentially leading to higher-paying opportunities.
Undertake Industry-Relevant Mini-Projects- (Semester 3)
Beyond coursework, actively seek out and complete mini-projects that address real-world challenges, ideally mentored by faculty or industry professionals. Participate in hackathons or Kaggle competitions. This builds a strong project portfolio and demonstrates initiative.
Tools & Resources
Kaggle, Hackathons, Industry mentors, Open-source datasets
Career Connection
Hands-on projects with industry relevance are crucial for showcasing practical skills during internships and placements, impressing recruiters from companies like TCS, Wipro, Infosys, and various AI startups.
Engage in Workshops and Networking- (Semester 3)
Attend departmental workshops, guest lectures, and industry seminars organized by the university or local tech communities. Network with faculty, alumni, and industry experts. This exposure keeps you updated on cutting-edge research and industry trends, opening doors to mentorship and opportunities.
Tools & Resources
University seminars, Industry conferences (virtual/local), LinkedIn for professional networking
Career Connection
Networking can lead to internship opportunities, valuable career advice, and potential job referrals. Staying abreast of industry trends ensures your skills remain relevant and highly sought after.
Advanced Stage
Excellence in M.Tech Dissertation- (Semester 4)
Devote significant time and effort to your M.Tech dissertation. Select a challenging and novel research problem, meticulously plan and execute your research, and produce a high-quality thesis. Aim for research publication in reputable conferences or journals if possible.
Tools & Resources
Research methodologies, Academic databases (Scopus, Web of Science), LaTeX for thesis writing
Career Connection
A strong dissertation demonstrates advanced research capabilities, critical thinking, and problem-solving skills. It can be a significant differentiator for R&D roles, academic positions, or admission to PhD programs.
Intensive Placement Preparation- (Semester 4)
Begin systematic preparation for campus placements well in advance. Practice technical questions (especially in AI/ML/Robotics), aptitude tests, and soft skills for group discussions and HR interviews. Utilize the university''''s career guidance cell for mock interviews and resume reviews.
Tools & Resources
Placement cell resources, Online coding platforms for interview prep, Mock interview sessions
Career Connection
Thorough preparation directly translates into better performance during placement drives, securing positions in leading companies in the AI and Robotics sector.
Professional Branding and Mentorship- (Semester 4)
Cultivate a strong professional online presence, especially on LinkedIn, showcasing your projects, skills, and academic achievements. Seek out mentors (faculty or industry professionals) who can guide your career path and provide insights into industry best practices and emerging opportunities.
Tools & Resources
LinkedIn, Professional portfolio websites, Mentorship programs
Career Connection
A strong professional brand and mentorship network are invaluable for long-term career growth, opening doors to advanced opportunities, leadership roles, and staying competitive in the rapidly evolving tech landscape.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in Computer Science and Engineering, Information Technology, Electronics and Telecommunication Engineering, Electrical Engineering, or equivalent relevant disciplines, with a valid GATE score. Specific criteria may apply per admission cycle.
Duration: 2 years (4 semesters)
Credits: 60 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTSIS-101 | Intelligent Systems & Knowledge Representation | Core | 4 | Artificial Intelligence Fundamentals, Knowledge Representation Techniques, Logic and Inference Systems, Semantic Networks and Frames, Expert Systems Development, Rule-Based Reasoning |
| MTSIS-102 | Machine Learning & Pattern Recognition | Core | 4 | Supervised Learning Algorithms, Unsupervised Learning Techniques, Neural Networks Basics, Deep Learning Concepts, Pattern Classification, Ensemble Learning Methods |
| MTSIS-103 | Advanced Data Structure & Algorithms | Core | 4 | Advanced Tree Structures, Graph Algorithms, Algorithm Design Paradigms, Dynamic Programming, Complexity Analysis, Randomized Algorithms |
| MTSIS-104(P) | Intelligent Systems Lab I | Lab | 2 | AI Search Algorithm Implementation, Prolog Programming, Machine Learning Tools Usage, Expert System Shells, Python for AI Applications |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTSIS-201 | Soft Computing | Core | 4 | Fuzzy Set Theory, Artificial Neural Networks Architectures, Genetic Algorithms, Evolutionary Computation, Hybrid Intelligent Systems, Swarm Intelligence |
| MTSIS-202 | Robotics | Core | 4 | Robot Kinematics and Dynamics, Robot Control Strategies, Sensors and Actuators in Robotics, Motion Planning and Navigation, Robot Vision and Image Processing, Industrial Robot Applications |
| MTSIS-203 | Research Methodology & Dissertation I | Core | 4 | Research Design and Planning, Literature Survey Techniques, Technical Writing and Presentation, Data Collection and Analysis Methods, Project Proposal Formulation, Research Ethics and Plagiarism |
| MTSIS-204(P) | Intelligent Systems Lab II | Lab | 2 | Fuzzy Logic Implementation, Neural Network Training and Testing, Genetic Algorithm Applications, Robot Simulation Software, Robot Operating System (ROS) Basics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTSIS-301 | Elective I | Elective | 4 | Natural Language Processing Models, Computer Vision Techniques, Data Mining Algorithms, Distributed Artificial Intelligence Concepts, Advanced AI Applications, Specialized Domain Methodologies |
| MTSIS-302 | Elective II | Elective | 4 | Agent Based Computing, Human Computer Interaction Principles, Cybernetics and Control Systems, Information Security & Steganography, Emerging Computing Paradigms, Advanced Security Aspects |
| MTSIS-303(P) | Intelligent Systems Lab III | Lab | 2 | Elective-specific Implementations, Advanced AI Project Development, Large Dataset Handling, Deep Learning Framework Usage, Research Software Application |
| MTSIS-304 | Dissertation II | Core | 6 | Project Execution and Implementation, Data Collection and Experimentation, Results Analysis and Interpretation, Preliminary Thesis Writing, Progress Presentation, Problem-Solving Strategies |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTSIS-401 | Dissertation III | Core | 16 | In-depth Research and Development, System Prototyping and Testing, Experimental Validation and Analysis, Comprehensive Thesis Submission, Viva-Voce Examination, Research Publication Strategies |
