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M-TECH in Computer Science And Engineering Data Science And Artificial Intelligence Computer Science And Engineering at Indian Institute of Technology Tirupati
Tirupati, Andhra Pradesh
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About the Specialization
What is Computer Science and Engineering (Data Science and Artificial Intelligence, Computer Science and Engineering) at Indian Institute of Technology Tirupati Tirupati?
This Data Science and Artificial Intelligence program at Indian Institute of Technology Tirupati focuses on equipping students with advanced theoretical knowledge and practical skills in cutting-edge AI and data science domains. With a strong emphasis on foundational algorithms, machine learning, deep learning, and big data technologies, the program prepares graduates for the rapidly evolving Indian tech landscape, where intelligent systems and data-driven decisions are paramount. It integrates rigorous academic learning with hands-on project experience.
Who Should Apply?
This program is ideal for engineering graduates (B.Tech/B.E.) in Computer Science, IT, or related disciplines, as well as MCA/M.Sc. (CS/IT) holders, who possess a strong aptitude for mathematics, programming, and a keen interest in AI/ML. It caters to fresh graduates aspiring to kickstart careers in data science, AI engineering, or research, and also to working professionals seeking to upskill and transition into leadership roles in AI-driven enterprises across India. A valid GATE score is a mandatory prerequisite.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths as Data Scientists, Machine Learning Engineers, AI Researchers, Big Data Analysts, or AI Architects within India''''s booming tech sector. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals commanding upwards of INR 20-40 LPA. The program fosters critical thinking, problem-solving, and innovation, preparing students for roles in product development, analytics, and R&D in both startups and established Indian and multinational corporations.
Student Success Practices
Foundation Stage
Master Core Algorithms and Math- (Semester 1-2)
Rigorously understand and implement fundamental data structures, algorithms, and mathematical concepts (linear algebra, probability, calculus) crucial for AI/ML. Focus on problem-solving platforms to build strong coding intuition.
Tools & Resources
LeetCode, HackerRank, GeeksforGeeks, Khan Academy, NPTEL courses on Algorithms and Mathematics
Career Connection
Essential for passing technical interviews at top tech companies and building efficient AI models.
Hands-on with Programming & Libraries- (Semester 1-2)
Become proficient in Python, R, and their respective data science libraries (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch). Engage in mini-projects to apply theoretical knowledge to practical scenarios.
Tools & Resources
Kaggle notebooks, Google Colab, Jupyter notebooks, Official documentation of libraries
Career Connection
Direct skill application for Data Scientist and Machine Learning Engineer roles.
Build a Strong Peer Network & Study Groups- (Semester 1-2)
Collaborate with classmates on assignments, discuss complex topics, and solve problems together. Form study groups to reinforce learning and gain diverse perspectives.
Tools & Resources
WhatsApp groups, Telegram channels, Academic forums, Campus discussion spaces
Career Connection
Develops communication and teamwork skills vital for industry, and provides a support system for academic challenges and future career exploration.
Intermediate Stage
Specialized Elective Depth & Project Work- (Semester 3)
Choose electives strategically to specialize in areas like Deep Learning, NLP, or Computer Vision. Undertake challenging projects, possibly with a faculty mentor, to apply advanced concepts and build a portfolio.
Tools & Resources
GitHub for project showcasing, Papers from leading conferences (NeurIPS, ICML, CVPR, ACL), Specialized online courses (Coursera, edX)
Career Connection
Creates a strong technical profile for specific AI/ML roles and demonstrates practical expertise.
Participate in AI/ML Competitions- (Semester 3)
Actively participate in Kaggle competitions, hackathons, or national/international AI challenges. This hones problem-solving skills under pressure and exposes you to diverse datasets and real-world problems.
Tools & Resources
Kaggle, HackerEarth, Google AI contests, University hackathon platforms
Career Connection
Boosts resume, provides practical experience, and can lead to networking opportunities with industry professionals and recruiters.
Seek Industry Internships & Research Exposure- (Semester 3)
Actively apply for internships at tech companies or engage in research projects at IITs/research labs. This provides invaluable industry exposure, mentorship, and helps refine career interests.
Tools & Resources
IIT Tirupati''''s career development cell, LinkedIn, Company career pages, Faculty recommendations
Career Connection
Converts theoretical knowledge into practical skills, builds professional networks, and significantly enhances placement prospects.
Advanced Stage
Deep Dive into Thesis/Major Project- (Semester 3-4)
Focus intensely on your M.Tech thesis or major project, aiming for novel contributions or significant practical implementations. Document your work meticulously and strive for research publication.
Tools & Resources
LaTeX for thesis writing, Academic research databases (IEEE Xplore, ACM Digital Library, arXiv), Research group meetings
Career Connection
Develops independent research skills, showcases specialized expertise, and can open doors to R&D roles or PhD studies.
Refine Interview Skills & Portfolio- (Semester 3-4)
Practice technical interviews, aptitude tests, and behavioral questions. Prepare a compelling portfolio of projects (GitHub, personal website) to showcase your skills effectively to potential employers.
Tools & Resources
InterviewBit, Glassdoor, Mock interviews with peers/mentors, LinkedIn profiles
Career Connection
Crucial for securing placements, demonstrating readiness for the professional world, and highlighting unique capabilities.
Network Strategically & Attend Conferences- (Semester 3-4)
Attend webinars, industry talks, and virtual/physical conferences (e.g., those by NASSCOM, TiE, or specific AI conferences) to network with professionals, stay updated on trends, and explore career opportunities.
Tools & Resources
LinkedIn, Conference websites, Industry meetups, Alumni network
Career Connection
Expands professional contacts, leads to potential job referrals, and provides insights into industry trends and growth areas.
Program Structure and Curriculum
Eligibility:
- B.Tech./B.E./AMIE/MCA/M.Sc. in Computer Science/IT or equivalent degree with a minimum of 60% aggregate marks (6.5 CGPA out of 10) for General/OBC-NCL/EWS category and 55% aggregate marks (6.0 CGPA out of 10) for SC/ST/PwD category candidates. GATE score in CS/EC/EE/MA/ST is mandatory.
Duration: 4 semesters / 2 years
Credits: 60 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS501 | Advanced Data Structures and Algorithms | Core | 3 | Algorithmic analysis, Advanced data structures (heaps, trees, hash tables), Graph algorithms, Dynamic programming, Greedy algorithms, Network flow |
| CS502 | Advanced Computer Architecture | Core | 3 | Pipelining, Instruction-level parallelism, Memory hierarchy, Cache coherence, Multiprocessors, Interconnection networks |
| CS503 | Mathematical Foundations of Computer Science | Core | 3 | Logic and proof techniques, Set theory, Relations and functions, Graph theory, Recurrence relations, Algebraic structures |
| CS504 | Research Methodology | Core | 2 | Research problem formulation, Literature review, Data collection and analysis, Experimental design, Technical writing, Research ethics |
| CS551 | Advanced Computing Laboratory | Core Lab | 2 | Advanced programming concepts, Data structures implementation, Algorithm design and analysis, Debugging techniques, Software development tools |
| Elective-I | Professional Elective I | Elective (DS&AI / General CSE) | 3 | Specialized topics in chosen domain, Advanced concepts, Problem-solving applications, Literature study, Project work |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| Elective-II | Professional Elective II | Elective (DS&AI / General CSE) | 3 | Specialized topics in chosen domain, Advanced concepts, Problem-solving applications, Literature study, Project work |
| Elective-III | Professional Elective III | Elective (DS&AI / General CSE) | 3 | Specialized topics in chosen domain, Advanced concepts, Problem-solving applications, Literature study, Project work |
| Elective-IV | Professional Elective IV | Elective (DS&AI / General CSE) | 3 | Specialized topics in chosen domain, Advanced concepts, Problem-solving applications, Literature study, Project work |
| Elective-V | Professional Elective V | Elective (DS&AI / General CSE) | 3 | Specialized topics in chosen domain, Advanced concepts, Problem-solving applications, Literature study, Project work |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| Elective-VI | Professional Elective VI | Elective (DS&AI / General CSE) | 3 | Specialized topics in chosen domain, Advanced concepts, Problem-solving applications, Literature study, Project work |
| Elective-VII | Professional Elective VII | Elective (DS&AI / General CSE) | 3 | Specialized topics in chosen domain, Advanced concepts, Problem-solving applications, Literature study, Project work |
| Elective-VIII | Professional Elective VIII | Elective (DS&AI / General CSE) | 3 | Specialized topics in chosen domain, Advanced concepts, Problem-solving applications, Literature study, Project work |
| CS598 | Project/Thesis Part-I | Project | 8 | Literature survey, Problem definition, Methodology design, Experimental setup, Initial results, Research proposal |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS599 | Project/Thesis Part-II | Project | 12 | Advanced experimentation, Data analysis, Model refinement, Results validation, Thesis writing, Research publication |
Semester electives
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS601 | Advanced Topics in Machine Learning | Elective (DS&AI) | 3 | Bayesian learning, Kernel methods, Graphical models, Ensemble methods, Reinforcement learning, Causality in ML |
| CS602 | Natural Language Processing | Elective (DS&AI) | 3 | Word embeddings, Sequence models (RNN, Transformers), Semantic parsing, Question answering, Text generation, Dialogue systems |
| CS603 | Deep Learning | Elective (DS&AI) | 3 | CNNs for vision, RNNs for sequences, Attention mechanisms, Generative adversarial networks (GANs), Autoencoders, Transformers |
| CS604 | Reinforcement Learning | Elective (DS&AI) | 3 | Policy iteration, Value iteration, Q-learning, SARSA, Actor-critic methods, Multi-agent reinforcement learning |
| CS605 | Computer Vision | Elective (DS&AI) | 3 | Image recognition, Object detection, Semantic segmentation, 3D vision, Facial recognition, Action recognition |
| CS606 | Big Data Analytics | Elective (DS&AI) | 3 | Distributed data processing (Spark, Flink), NoSQL databases, Data stream analytics, Machine learning on big data, Cloud data platforms |
| CS607 | Data Mining | Elective (DS&AI) | 3 | Pattern discovery, Predictive modeling, Clustering algorithms (K-means, hierarchical), Association rules, Outlier detection |
| CS608 | Probabilistic Graphical Models | Elective (DS&AI) | 3 | Bayesian networks, Markov random fields, Inference algorithms (belief propagation), Learning parameters, Variational inference |
| CS609 | Information Retrieval | Elective (DS&AI) | 3 | Boolean retrieval, Vector space model, Ranking algorithms, Query processing, Web search, Evaluation metrics |
| CS610 | Recommender Systems | Elective (DS&AI) | 3 | Collaborative filtering, Content-based filtering, Hybrid approaches, Matrix factorization, Deep learning for recommendations, Evaluation |
| CS611 | Time Series Analysis | Elective (DS&AI) | 3 | ARIMA models, Exponential smoothing, Spectral analysis, Forecasting, State-space models, Deep learning for time series |
| CS612 | Optimization for Machine Learning | Elective (DS&AI) | 3 | Gradient descent, Stochastic gradient descent, Convex optimization, Constrained optimization, Lagrangian duality, Karush-Kuhn-Tucker conditions |
| CS613 | Image Processing | Elective (DS&AI) | 3 | Image enhancement, Image restoration, Feature detection, Image segmentation, Geometric transformations, Medical image analysis |
| CS614 | Speech Processing | Elective (DS&AI) | 3 | Speech signal analysis, Feature extraction (MFCC), Phonetics, Hidden Markov Models (HMMs), Automatic speech recognition, Text-to-speech synthesis |
| CS615 | Brain-Computer Interfacing | Elective (DS&AI) | 3 | EEG/ECoG signal acquisition, Signal processing, Feature extraction, Classification algorithms, Real-time BCI systems, Applications |
| CS616 | Knowledge Representation and Reasoning | Elective (DS&AI) | 3 | Ontologies, Description logics, Semantic Web, Rule-based systems, Non-monotonic reasoning, Logic programming |
| CS617 | AI Ethics and Governance | Elective (DS&AI) | 3 | Fairness, Accountability, Transparency in AI, Bias detection, Privacy concerns, Regulatory frameworks, Societal impact of AI |
| CS618 | Human-AI Interaction | Elective (DS&AI) | 3 | AI explainability, Trust in AI, User interfaces for AI, Collaborative AI, Ethical considerations in HRI, Design principles for AI systems |
| CS619 | Federated Learning | Elective (DS&AI) | 3 | Distributed machine learning, Privacy-preserving AI, Communication efficiency, Aggregation mechanisms, Security in federated learning |
| CS620 | Explainable AI | Elective (DS&AI) | 3 | Interpretability vs explainability, Model-agnostic explanations (LIME, SHAP), Model-specific explanations, Counterfactual explanations, Ethical implications |
| CS621 | Generative Models | Elective (DS&AI) | 3 | Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion models, Flow-based models, Image generation, Text generation |
| CS622 | IoT and Edge AI | Elective (DS&AI) | 3 | Edge computing architectures, AI model deployment on edge devices, Resource optimization, Sensor data processing, Distributed intelligence |
| CS623 | Quantum Machine Learning | Elective (DS&AI) | 3 | Quantum algorithms for ML, Quantum neural networks, Quantum support vector machines, Quantum data encoding, Hybrid quantum-classical ML |
| CS624 | Biometrics | Elective (DS&AI) | 3 | Fingerprint recognition, Face recognition, Iris recognition, Voice biometrics, Multi-modal biometrics, Security and privacy in biometrics |
Semester electives
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS511 | Advanced Operating Systems | Elective (General CSE) | 3 | Distributed OS, Real-time OS, Microkernel architectures, OS security, Virtualization, File systems |
| CS512 | Advanced Database Systems | Elective (General CSE) | 3 | Query optimization, Transaction management, Distributed databases, NoSQL databases, Data warehousing, Big data storage |
| CS513 | Advanced Computer Networks | Elective (General CSE) | 3 | Network architectures, Routing protocols, Congestion control, Software-defined networking, Network security, Wireless networks |
| CS514 | Distributed Systems | Elective (General CSE) | 3 | Distributed consensus, Fault tolerance, Distributed transactions, Cloud computing architectures, Peer-to-peer systems, Message passing |
| CS515 | Cloud Computing | Elective (General CSE) | 3 | Cloud service models (IaaS, PaaS, SaaS), Virtualization, Cloud storage, Cloud security, Serverless computing, Containerization |
| CS516 | Software Engineering | Elective (General CSE) | 3 | Software lifecycle, Requirements engineering, Design patterns, Software testing, Project management, Agile methodologies |
| CS517 | Compiler Design | Elective (General CSE) | 3 | Lexical analysis, Parsing, Semantic analysis, Intermediate code generation, Code optimization, Runtime environment |
| CS518 | Formal Methods in Software Engineering | Elective (General CSE) | 3 | Logic for specifications, Formal specification languages, Model checking, Program verification, Theorem proving, Z-notation |
| CS519 | Advanced Wireless Networks | Elective (General CSE) | 3 | Mobile ad-hoc networks, Wireless sensor networks, 5G architectures, Cognitive radio, IoT communication protocols, Security in wireless |
| CS520 | Secure Programming | Elective (General CSE) | 3 | Buffer overflows, Input validation, SQL injection, Cross-site scripting, Cryptographic best practices, Secure coding guidelines |
| CS521 | Information Security | Elective (General CSE) | 3 | Cryptography, Access control, Network security, Application security, Security policies, Incident response |
| CS522 | Cryptography and Network Security | Elective (General CSE) | 3 | Symmetric and asymmetric encryption, Hashing, Digital signatures, Key management, VPNs, Firewalls |
| CS523 | High-Performance Computing | Elective (General CSE) | 3 | Parallel architectures, GPU programming, Message Passing Interface (MPI), OpenMP, Performance analysis, Distributed memory systems |
| CS524 | Parallel Computing | Elective (General CSE) | 3 | Parallel programming models, Shared memory, Distributed memory, Synchronization, Performance metrics, Parallel algorithms |
| CS525 | Internet of Things | Elective (General CSE) | 3 | IoT architecture, Sensors and actuators, Communication protocols (MQTT, CoAP), Edge computing, IoT security, Smart applications |
| CS526 | Quantum Computing | Elective (General CSE) | 3 | Quantum mechanics fundamentals, Qubits, Quantum gates, Quantum algorithms (Shor''''s, Grover''''s), Quantum entanglement, Quantum cryptography |
| CS527 | Natural Language Processing | Elective (General CSE) | 3 | Text preprocessing, Part-of-speech tagging, Syntactic parsing, Semantic analysis, Machine translation, Sentiment analysis |
| CS528 | Computer Vision | Elective (General CSE) | 3 | Image formation, Feature extraction, Image segmentation, Object recognition, Motion analysis, Deep learning for vision |
| CS529 | Bioinformatics | Elective (General CSE) | 3 | Sequence alignment, Phylogenetics, Gene prediction, Protein structure prediction, Microarray data analysis, Biological databases |
| CS530 | Theory of Computation | Elective (General CSE) | 3 | Finite automata, Regular languages, Context-free grammars, Turing machines, Decidability, Complexity classes (P, NP) |
| CS531 | Program Analysis and Verification | Elective (General CSE) | 3 | Static analysis, Dynamic analysis, Abstract interpretation, Symbolic execution, Model checking, Formal verification |
| CS532 | Topics in Theoretical Computer Science | Elective (General CSE) | 3 | Advanced complexity theory, Randomized algorithms, Approximation algorithms, Cryptographic foundations, Graph algorithms |
| CS533 | Advanced Graph Theory | Elective (General CSE) | 3 | Graph traversal, Connectivity, Matching, Coloring, Network flow, Planar graphs |
| CS534 | Advanced Topics in Algorithms | Elective (General CSE) | 3 | Amortized analysis, Randomized algorithms, Approximation algorithms, Online algorithms, Computational geometry, String algorithms |
| CS535 | Computational Complexity Theory | Elective (General CSE) | 3 | Time and space complexity, NP-completeness, Hierarchy theorems, Randomized complexity, Interactive proofs, Quantum complexity |
| CS536 | Advanced Artificial Intelligence | Elective (General CSE) | 3 | Search algorithms, Knowledge representation, Logical reasoning, Planning, Learning paradigms, Expert systems |
| CS537 | Reinforcement Learning | Elective (General CSE) | 3 | Markov decision processes, Dynamic programming, Monte Carlo methods, Temporal difference learning, Policy gradient, Deep reinforcement learning |
| CS538 | Data Mining | Elective (General CSE) | 3 | Data preprocessing, Association rule mining, Classification, Clustering, Anomaly detection, Ensemble methods |
| CS539 | Machine Learning | Elective (General CSE) | 3 | Supervised learning, Unsupervised learning, Model evaluation, Regression, Classification (SVM, Decision Trees), Neural networks |
| CS540 | Deep Learning | Elective (General CSE) | 3 | Neural network architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Optimization techniques, Regularization, Transfer learning |
| CS541 | Big Data Analytics | Elective (General CSE) | 3 | Hadoop ecosystem (HDFS, MapReduce), Spark, Stream processing, Data visualization, Data governance, NoSQL databases |
| CS542 | Blockchain Technologies | Elective (General CSE) | 3 | Cryptographic primitives, Distributed ledgers, Consensus mechanisms, Smart contracts, Bitcoin, Ethereum, Blockchain applications |
| CS543 | Advanced Compiler Design | Elective (General CSE) | 3 | Control flow analysis, Data flow analysis, Loop optimization, Register allocation, Just-in-time compilation, Garbage collection |
| CS544 | Virtual Reality | Elective (General CSE) | 3 | VR devices, 3D graphics, Tracking technologies, Haptic feedback, VR development platforms, User experience design |
| CS545 | Augmented Reality | Elective (General CSE) | 3 | AR hardware, Computer vision for AR, Tracking and registration, AR SDKs, Mixed reality, AR applications |
| CS546 | Game Theory | Elective (General CSE) | 3 | Strategic form games, Extensive form games, Nash equilibrium, Mechanism design, Cooperative games, Evolutionary game theory |
| CS547 | Human-Computer Interaction | Elective (General CSE) | 3 | User-centered design, Usability testing, Interaction models, Interface design principles, Accessibility, Cognitive psychology in HCI |
| CS548 | Cognitive Computing | Elective (General CSE) | 3 | Cognitive architectures, Natural language understanding, Machine perception, Learning from data, Human-like reasoning, Applications of cognitive systems |
| CS549 | Robotics | Elective (General CSE) | 3 | Robot kinematics, Dynamics, Motion planning, Control systems, Sensors and actuators, Robot vision |
| CS550 | Cyber Physical Systems | Elective (General CSE) | 3 | Embedded systems, Sensor networks, Real-time systems, Control theory, Security of CPS, Applications (smart grid, autonomous vehicles) |
| CS591 | Independent Study | Elective (General CSE) | 3 | Advanced research topic, Literature review, Problem formulation, Methodology development, Report writing |
