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M-TECH in Signal Processing And Machine Learning at National Institute of Technology Karnataka, Surathkal
Dakshina Kannada, Karnataka
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About the Specialization
What is Signal Processing and Machine Learning at National Institute of Technology Karnataka, Surathkal Dakshina Kannada?
This Signal Processing and Machine Learning program at NITK, Mangaluru, focuses on equipping students with advanced theoretical and practical knowledge in the fields of digital signal processing, machine learning algorithms, and their diverse applications. It caters to the growing demand for skilled professionals who can design and implement intelligent systems for data analysis, pattern recognition, and decision-making in various Indian industries.
Who Should Apply?
This program is ideal for engineering graduates, particularly from EEE, ECE, CSE, and IT backgrounds, seeking entry into cutting-edge AI/ML and signal processing roles. It also suits working professionals aiming to upskill in areas like data analytics, computer vision, and speech processing, or career changers looking to transition into the rapidly evolving Indian tech landscape, requiring a strong foundation in mathematics and programming.
Why Choose This Course?
Graduates of this program can expect to secure roles as AI/ML engineers, data scientists, DSP specialists, or research engineers in leading Indian IT firms, startups, and R&D centers. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning significantly more. The program prepares students for advanced research, product development, and offers a strong foundation for professional certifications in AI/ML.
Student Success Practices
Foundation Stage
Master Advanced Core Concepts- (Semester 1-2)
Diligently focus on understanding the advanced theoretical underpinnings of DSP, Machine Learning, and associated mathematics. Utilize NPTEL, Coursera, and reference books to deepen knowledge beyond classroom lectures. Actively solve problems and work through examples.
Tools & Resources
NPTEL courses on Advanced DSP/ML, Standard textbooks like ''''Digital Signal Processing'''' by Proakis, ''''Deep Learning'''' by Goodfellow, Online coding platforms for practice
Career Connection
A strong theoretical base is critical for solving complex real-world problems and excelling in technical interviews for specialized roles.
Develop Strong Programming & Simulation Skills- (Semester 1-2)
Consistently practice implementing algorithms and concepts using Python (with libraries like NumPy, SciPy, Scikit-learn, TensorFlow/PyTorch) and MATLAB. Focus on efficient coding, debugging, and data visualization. Participate in coding competitions.
Tools & Resources
HackerRank, LeetCode for competitive programming, Kaggle for data science challenges, GitHub for project version control
Career Connection
Hands-on coding proficiency is a primary requirement for all ML/DSP engineering roles and demonstrates practical problem-solving abilities.
Engage in Departmental Research Seminars & Workshops- (Semester 1-2)
Attend all departmental seminars, workshops, and guest lectures by industry experts or senior researchers. This exposes you to ongoing research trends, cutting-edge applications, and helps identify potential project areas.
Tools & Resources
Departmental notice boards, Institute events calendar, LinkedIn for industry professional talks
Career Connection
Fosters research aptitude, helps in choosing relevant project topics, and provides networking opportunities with faculty and industry professionals.
Intermediate Stage
Proactively Choose & Deep Dive into Electives- (Semester 3)
Select electives that align with your specific interests and career goals (e.g., Computer Vision, NLP, Medical Imaging, Speech Processing). Go beyond the syllabus, undertake extra reading, and work on small projects related to the elective topics.
Tools & Resources
ArXiv for latest research papers, Specific libraries for chosen domain (e.g., OpenCV for Vision, SpaCy for NLP), Online specialized courses
Career Connection
Enables deeper specialization, making you a more attractive candidate for niche roles in specific ML/DSP domains.
Initiate and Progress on M.Tech Project Work Part 1- (Semester 3)
Start your project work early by conducting a thorough literature survey, identifying a challenging problem, and meticulously designing your methodology. Regularly meet with your supervisor and seek early feedback on your approach and preliminary results.
Tools & Resources
IEEE Xplore, Google Scholar, ResearchGate for literature, Project management tools (e.g., Trello), Version control with Git
Career Connection
This is your flagship work. A strong project showcases research capability, problem-solving skills, and deep domain knowledge, crucial for both industry and further academic pursuits.
Seek Industry Internships or Research Collaborations- (Semester 3 (during summer break or part-time))
Actively look for summer internships or research collaborations with companies or other research labs during your M.Tech breaks or alongside your studies. This provides invaluable real-world experience and networking opportunities.
Tools & Resources
LinkedIn Jobs, Internshala, Company career pages, Faculty recommendations
Career Connection
Internships are often a direct path to pre-placement offers, provide industry exposure, and enhance your resume significantly.
Advanced Stage
Refine & Finalize M.Tech Project Work Part 2 and Thesis- (Semester 4)
Dedicate ample time to rigorous experimentation, performance evaluation, and detailed analysis for your M.Tech project. Focus on clear, concise thesis writing, ensuring all contributions are well-documented and presented. Prepare thoroughly for the thesis defense.
Tools & Resources
LaTeX for thesis writing, Academic writing resources, Presentation software (PowerPoint/Beamer)
Career Connection
A high-quality thesis demonstrates your ability to conduct independent research, critical thinking, and impactful contribution – highly valued in R&D and advanced engineering roles.
Intensive Placement Preparation & Mock Interviews- (Semester 4)
Start placement preparation early. Practice technical questions related to DSP, ML, data structures, and algorithms. Engage in mock interviews (HR and technical) with peers and seniors. Tailor your resume and cover letter to specific job descriptions.
Tools & Resources
InterviewBit, GeeksforGeeks, Glassdoor for company-specific interview experiences, NITK placement cell resources
Career Connection
Effective preparation maximizes your chances of securing desired roles in top companies through the campus placement process.
Build a Professional Network and Personal Brand- (Semester 4 and ongoing)
Connect with alumni, industry professionals, and faculty on platforms like LinkedIn. Share your project work and insights. Attend industry conferences or workshops to expand your network and stay updated on professional trends.
Tools & Resources
LinkedIn, Professional body memberships (e.g., IEEE), Personal website/blog to showcase projects
Career Connection
Networking can open doors to opportunities beyond campus placements, provide mentorship, and support long-term career growth in India''''s competitive tech landscape.
Program Structure and Curriculum
Eligibility:
- B.E. / B.Tech. in Electrical & Electronics Engg. / Electronics & Communication Engg. / Telecommunication Engg. / Computer Science & Engg. / Information Technology / Instrumentation & Control Engg. / Electronics & Instrumentation Engg. / Instrumentation Engg. / Biomedical Engg. / Medical Electronics or equivalent degree with an aggregate of 6.5 CGPA or 60% marks.
Duration: 4 semesters / 2 years
Credits: 82 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EEL701 | Advanced Digital Signal Processing | Core | 4 | Discrete-time signals and systems, DFT and FFT algorithms, Digital filter design (IIR, FIR), Adaptive filters (LMS, RLS), Multirate DSP and applications |
| EEL702 | Advanced Mathematics for Engineers | Core | 4 | Linear Algebra and vector spaces, Optimization techniques, Probability and random variables, Stochastic processes, Estimation theory |
| EEL703 | Advanced Digital Communication | Core | 4 | Digital modulation schemes, Optimum receivers and matched filters, Channel coding techniques, Spread spectrum communication, OFDM and MIMO systems |
| EEL704 | Machine Learning | Core | 4 | Supervised learning algorithms, Unsupervised learning methods, Deep learning fundamentals, Neural networks and backpropagation, Support Vector Machines |
| EEL705 | Signal Processing and Machine Learning Lab | Lab | 2 | MATLAB/Python for DSP algorithms, Filter design and spectral analysis, Machine learning algorithm implementation, Image processing operations, Data analysis and visualization |
| EEL721 | Estimation and Detection Theory | Elective - I (Example) | 4 | Hypothesis testing, Parameter estimation (MLE, MAP), Cramer-Rao lower bound, Wiener and Kalman filters, Likelihood ratio tests |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EEL751 | Adaptive Signal Processing | Core | 4 | Adaptive filter structures, LMS algorithm and its variations, Recursive Least Squares (RLS) algorithm, Adaptive equalization, System identification and noise cancellation |
| EEL752 | Digital Image and Video Processing | Core | 4 | Image enhancement and restoration, Image segmentation and feature extraction, Image compression standards, Video fundamentals and motion estimation, Video compression techniques |
| EEL753 | Deep Learning | Core | 4 | Feedforward neural networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Deep reinforcement learning |
| EEL761 | Artificial Neural Networks | Elective - II (Example) | 4 | Perceptron and its limitations, Multilayer perceptrons, Backpropagation algorithm, Radial Basis Function Networks, Self-Organizing Maps |
| EEL765 | Pattern Recognition | Elective - III (Example) | 4 | Feature extraction and selection, Classification techniques, Clustering algorithms, Discriminant functions, Hidden Markov Models |
| EEL754 | Soft Computing and Machine Intelligence Lab | Lab | 2 | Neural network implementation using frameworks, Fuzzy logic system design, Genetic algorithms for optimization, Deep learning model development, Pattern recognition applications |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EEL801 | M.Tech Project Work - Part 1 | Project | 12 | Literature survey and problem identification, Research methodology design, Preliminary implementation and data collection, Analysis of initial results, Technical report writing |
| EEL820 | Speech Processing | Elective - IV (Example) | 3 | Speech production and perception models, Speech analysis techniques (LPC, MFCC), Speech recognition systems, Speech synthesis methods, Audio signal processing |
| EEL822 | Medical Image Processing | Elective - V (Example) | 3 | Medical image acquisition modalities, Image enhancement and noise reduction, Image segmentation in medical applications, Image registration techniques, 3D visualization and analysis |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EEL851 | M.Tech Project Work - Part 2 | Project | 20 | Advanced implementation and system development, Rigorous experimental validation and testing, Comprehensive results analysis and interpretation, Thesis writing and documentation, Project presentation and defense |
