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M-TECH-GENERAL in Machine Learning Under Ece at Indraprastha Institute of Information Technology Delhi
Delhi, Delhi
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About the Specialization
What is Machine Learning (under ECE) at Indraprastha Institute of Information Technology Delhi Delhi?
This Machine Learning program at Indraprastha Institute of Information Technology Delhi focuses on equipping students with advanced theoretical foundations and practical skills in AI. With India''''s rapid digital transformation, there''''s immense demand for ML experts across sectors like finance, healthcare, and e-commerce. The program''''s blend of core ECE and specialized ML courses provides a unique edge, addressing critical industry needs in the Indian market.
Who Should Apply?
This program is ideal for engineering graduates with a strong mathematical and programming background, especially those from ECE, CS, IT, or related fields, seeking entry into high-demand AI roles. It also suits working professionals who wish to upskill or pivot into machine learning, leveraging their existing technical expertise. Aspiring researchers and innovators looking to contribute to cutting-edge AI advancements in India will also find this program highly beneficial.
Why Choose This Course?
Graduates of this program can expect to secure roles as ML Engineers, Data Scientists, AI Researchers, or Computer Vision Engineers in top Indian and multinational companies like TCS, Wipro, Infosys, Google, Microsoft, and various startups. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals earning INR 25-50+ LPA. The comprehensive curriculum prepares students for global certifications and leadership roles in India''''s booming AI ecosystem.
Student Success Practices
Foundation Stage
Master Core Mathematical & Programming Fundamentals- (Semester 1-2)
Dedicate significant time to reinforce advanced probability, linear algebra, calculus, and programming skills (Python, data structures, algorithms). Utilize online platforms like Coursera (Mathematics for Machine Learning Specialization), Khan Academy, and competitive programming sites (HackerRank, LeetCode) to build a robust foundation.
Tools & Resources
Coursera (Mathematics for Machine Learning), Khan Academy, HackerRank, LeetCode, NPTEL courses on Probability and Linear Algebra
Career Connection
A strong grasp of these fundamentals is crucial for understanding complex ML algorithms and excelling in technical interviews for data science and ML engineering roles.
Engage Actively in Peer Learning and Study Groups- (Semester 1-2)
Form study groups with classmates to discuss challenging concepts, collaborate on assignments, and prepare for exams. Teaching peers helps solidify your understanding and exposes you to different problem-solving approaches. Participate in departmental seminars and workshops for broader exposure.
Tools & Resources
IIIT Delhi Student Forums, Discord/WhatsApp Study Groups, Departmental Seminar Series
Career Connection
Enhances problem-solving skills, builds communication abilities, and expands your professional network, all valuable assets in team-oriented industry environments.
Start Building a Portfolio with Mini-Projects- (Semester 1-2)
Apply newly learned concepts by undertaking small, self-driven machine learning projects. Use publicly available datasets from platforms like Kaggle. Document your code and methodologies thoroughly on GitHub. Focus on clear problem statements, data cleaning, model selection, and basic evaluation.
Tools & Resources
Kaggle, GitHub, Google Colab, Scikit-learn
Career Connection
Demonstrates practical application skills to potential employers, making your resume stand out for internships and entry-level positions.
Intermediate Stage
Seek Research Assistantships & Industry Internships- (Semester 2-3)
Actively look for Research Assistant (RA) positions under faculty working in your area of interest (e.g., Deep Learning, NLP, Computer Vision). Simultaneously, apply for summer internships at companies to gain practical industry exposure and understand real-world ML challenges. Leverage IIIT Delhi''''s strong industry connections.
Tools & Resources
IIIT Delhi Career Services, LinkedIn, Company career pages (Google, Microsoft, startups), Professor''''s lab websites
Career Connection
Provides invaluable experience, strengthens your resume, and often leads to pre-placement offers (PPOs) or strong recommendations for future roles.
Participate in AI/ML Competitions & Hackathons- (Semester 2-3)
Engage in data science competitions on platforms like Kaggle, Analytics Vidhya, or take part in hackathons organized by IIIT Delhi or external tech companies. This enhances your problem-solving under pressure, allows you to experiment with diverse datasets, and build collaborative skills.
Tools & Resources
Kaggle Competitions, Analytics Vidhya, IIIT Delhi ACM/IEEE Student Chapters
Career Connection
Develops a competitive portfolio, showcases your ability to deliver solutions, and attracts attention from recruiters scouting for talent.
Specialize and Build Expertise in a Niche Area- (Semester 2-3)
Beyond core ML, identify a specific area within Machine Learning (e.g., Reinforcement Learning, Generative AI, MLOps, Explainable AI) that genuinely interests you. Take specialized electives, read research papers, and work on advanced projects in this domain. Attend workshops and conferences related to your chosen niche.
Tools & Resources
arXiv.org, Top-tier ML conferences (NeurIPS, ICML, CVPR), Specialized MOOCs on Coursera/edX
Career Connection
Positions you as a subject matter expert, opening doors to highly specialized and higher-paying roles, and potentially research-oriented careers.
Advanced Stage
Focus on Dissertation/M.Tech Project for Industry Readiness- (Semester 3-4)
Your M.Tech Project (MTP) is the cornerstone of your learning. Choose a project with real-world applicability, ideally in collaboration with an industry partner or a strong research group. Focus on end-to-end implementation, rigorous evaluation, and clear documentation. Aim for a high-quality publication or a deployable solution.
Tools & Resources
IIIT Delhi Research Labs, Industry Collaboration opportunities, Jupyter Notebooks, TensorFlow/PyTorch
Career Connection
A strong MTP acts as a capstone project, directly showcasing your advanced skills and problem-solving capabilities to potential employers during final placements and interviews.
Intensive Placement Preparation and Mock Interviews- (Semester 3-4)
Begin comprehensive preparation for placements well in advance. Practice coding challenges (Data Structures and Algorithms) extensively. Conduct mock interviews focused on ML concepts, system design, and behavioral questions. Utilize IIIT Delhi''''s career services for resume reviews and interview coaching.
Tools & Resources
GeeksforGeeks, InterviewBit, LeetCode, IIIT Delhi Career Development Centre
Career Connection
Crucial for converting interview opportunities into job offers from top-tier companies, maximizing your chances for desired roles and compensation packages.
Build a Professional Network and Personal Brand- (Semester 3-4)
Attend industry events, tech talks, and alumni meetups. Connect with professionals, mentors, and recruiters on LinkedIn. Actively contribute to open-source projects or write technical blogs to establish your expertise and personal brand in the ML community. Your network is vital for career growth in the long run.
Tools & Resources
LinkedIn, Medium/Hashnode for blogging, Open-source ML projects on GitHub, IIIT Delhi Alumni Network
Career Connection
Opens doors to future job opportunities, collaborations, mentorship, and leadership roles, significantly impacting long-term career trajectory.
Program Structure and Curriculum
Eligibility:
- B.Tech./B.E./M.Sc./MCA degree in CS/IT/ECE/EE/Maths/Physics or equivalent. Candidates should have obtained at least 70% marks or 7.5 CGPA in their qualifying degree. Candidates must have a valid GATE score.
Duration: 4 semesters / 2 years
Credits: 64 Credits
Assessment: Internal: undefined, External: undefined
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ML 501 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Model Evaluation and Validation, Ensemble Methods, Feature Engineering, Introduction to Neural Networks |
| EC 503 | Advanced Probability and Stochastic Processes | Core | 3 | Random Variables and Distributions, Stochastic Processes Fundamentals, Markov Chains and Processes, Renewal Processes, Queueing Theory, Statistical Inference |
| EC 501 | Advanced Digital Signal Processing | Core | 3 | Discrete-Time Signals and Systems, Z-Transforms and DFT, FIR and IIR Filter Design, Multirate Signal Processing, Adaptive Filtering, Spectral Estimation |
| ML 502 | Deep Learning | Elective | 3 | Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Generative Models (GANs, VAEs), Deep Learning Frameworks |
| ML 503 | Optimization for Machine Learning | Elective | 3 | Convex Optimization, Gradient Descent and Variants, Stochastic Optimization, Constrained Optimization, Dual Ascent Methods, Non-convex Optimization Techniques |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ML 505 | Natural Language Processing | Elective | 3 | Text Preprocessing and Tokenization, Word Embeddings and Language Models, Syntactic and Semantic Analysis, Machine Translation, Text Classification and Summarization, Deep Learning for NLP |
| ML 506 | Computer Vision | Elective | 3 | Image Processing Fundamentals, Feature Detection and Description, Object Recognition and Detection, Image Segmentation, Multiple View Geometry, Deep Learning for Vision |
| ML 508 | Probabilistic Graphical Models | Elective | 3 | Bayesian Networks, Markov Random Fields, Inference Algorithms, Learning in Graphical Models, Variational Inference, Approximate Inference |
| MTP I | M.Tech Project I | Project | 6 | Problem Formulation, Literature Review, Methodology Design, Initial Implementation, Data Collection and Analysis, Project Proposal |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ML 504 | Advanced Reinforcement Learning | Elective | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal-Difference Learning, Policy Gradient Methods, Multi-Agent Reinforcement Learning |
| ML 510 | Explainable AI | Elective | 3 | Interpretability vs Explainability, Local and Global Explanations, LIME and SHAP, Adversarial Robustness, Fairness and Bias in AI, Causal Inference for Explainability |
| ML 512 | Big Data Analytics | Elective | 3 | Distributed Computing (Hadoop, Spark), Data Storage and Processing Frameworks, NoSQL Databases, Stream Processing, Scalable Machine Learning Algorithms, Data Warehousing and Lakes |
| ML 518 | Machine Learning Systems | Elective | 3 | MLOps Principles, Data Pipelines for ML, Model Deployment and Monitoring, Scalability and Performance, Cloud-based ML Platforms, Ethical AI System Design |
| MTP II | M.Tech Project II | Project | 6 | Algorithm Development, Extensive Experimentation, Performance Evaluation, Result Analysis, Prototype Development, Mid-Term Review and Report |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ML 507 | Generative Models | Elective | 3 | Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Flow-based Models, Diffusion Models, Autoregressive Models, Applications in Image/Text Generation |
| ML 509 | Causality and Machine Learning | Elective | 3 | Causal Inference Fundamentals, Causal Graphs and Models, Do-Calculus, Counterfactuals, Mediation Analysis, Applications in Decision Making |
| MTP III | M.Tech Project III | Project | 6 | Final System Integration, Extensive Testing and Validation, Dissertation Writing, Presentation and Defense Preparation, Impact Analysis, Publication Readiness |
