Online M.Tech in Signal Processing and Artificial Intelligence

Applicants should hold a B.E./B.Tech or equivalent degree in EEE, ECE or an equivalent allied engineering discipline. Final discipline equivalence is reviewed by the admissions team.

The programme is intended for working professionals, and candidates are generally expected to be employed at the time of admission. Applicants with prior work experience who are currently on a career break may be considered on a case-by-case basis.

Fresh graduates are not eligible for the programme. Applicants should have relevant professional experience or be currently employed.

Applicants should typically have at least 60% aggregate marks or a CGPA of 6.0 in graduation. For SC/ST/PwD applicants, the stated requirement is 55% or CGPA 5.5.

You may submit your application for review. The admissions team will assess whether your academic background is relevant to the programme.

Classes are delivered online through a combination of live interactive sessions and recorded learning modules.

The learning schedule is designed around working professionals, with sessions scheduled after working hours on weekdays and/or weekends. The final academic calendar is shared with enrolled learners.

Yes. Recorded modules support flexible, asynchronous learning and help learners revisit concepts alongside their live sessions.

Live sessions are an important part of the programme because they enable faculty interaction, peer learning and doubt resolution. Recorded content supports revision and self-paced learning; attendance requirements will be shared in the academic guidelines.

It means you learn advanced concepts and apply them to practical engineering challenges, workplace contexts, labs and project-based assignments rather than learning only through theory.

The programme builds strong foundations in Linear Algebra, Probability and Statistics, Machine Learning, Signal Processing, Computer Vision, Optimisation and Data Analytics.

Elective options include Advanced Time Series Forecasting, Statistical Signal Processing, Speech and Audio Processing, Multimedia Systems, Fourier and Wavelet Analysis, Time Frequency Analysis, Medical Imaging and Informatics, Architectures for Signal Processing and Machine Learning, and Topics in Signal Processing and Nonlinear Dynamics, Chaos and Fractals.

You will complete four electives across the later semesters, enabling you to build depth in areas aligned with your professional interests.

Yes. Generative AI is offered as an elective, covering foundational concepts, transformers, attention mechanisms, prompt engineering, fine-tuning, responsible AI and related applications.

Yes. The curriculum is relevant for professionals working in AI/ML, signal processing, wireless communication, embedded systems, computer vision, automotive, healthcare technology and related domains.

Yes. The programme includes labs, virtual learning environments, assignments, project reviews and practical problem-solving activities.

Assessment includes proctored evaluations, lab work, assignments, project reviews and viva-based evaluation.

The major project is a substantial two-semester project completed during the final stages of the programme. It is designed to help you solve an academic or industry-driven challenge and build a meaningful portfolio outcome.

Yes. Where appropriate, learners may work on industry-aligned or workplace-relevant challenges. The project is supported by an academic mentor from NITK.

Yes. You will receive academic mentorship from NITK faculty. The project may also require guidance from a domain expert from your workplace or relevant industry context.

The programme is delivered with guidance from NITK faculty, with learning support across areas such as signal processing, computer vision, machine learning, speech processing, biomedical signals and related disciplines.

The academic delivery is online. However, optional campus immersion opportunities may be offered for networking and experiential learning at the NITK Surathkal campus.

No. Campus immersion is optional. Details regarding format, schedule and participation will be shared after enrolment.

Learners become part of the NITK Alumni Network, enabling opportunities to stay connected with the broader NITK community.

Yes. You will learn alongside professionals from diverse engineering and technology backgrounds through live classes, projects and optional campus engagement opportunities.

The process typically includes submitting an online application, eligibility review, admission decision, document verification and fee payment to confirm your seat.

The admissions team will share the final document checklist during the application process. Applicants should keep their academic records, professional details and employment-related documents ready.

For domestic applicants, the stated total tuition fee is ₹3,25,000, with a one-time registration fee of ₹2,000. Fees are subject to the official admission communication for the relevant intake.

The published fee structure indicates a semester-wise amount of ₹81,250. Please confirm the current payment schedule and any available financing options with the admissions team before applying.

No. There’s no entrance test or interview, your qualifying degree and professional profile are reviewed against the programme eligibility criteria, and any additional requirement is communicated by the admissions team.

The programme can support progression toward roles such as Signal Processing Engineer, AI/ML Engineer, Computer Vision Engineer, Speech and Audio AI Engineer, Biomedical Signal Processing Engineer, Embedded AI/DSP Engineer and AI Research Engineer.

The curriculum is relevant across telecom and wireless, semiconductor and embedded systems, automotive ADAS (Advanced Driver Assistance System), defence and aerospace, healthcare technology, medical imaging and AI compute domains.

The programme is an advanced academic and applied-learning pathway that strengthens technical depth, portfolio quality and professional credibility. It is not a placement or salary guaranteed programme.