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B-TECH in Artificial Intelligence at National Institute of Technology Karnataka, Surathkal
Dakshina Kannada, Karnataka
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About the Specialization
What is Artificial Intelligence at National Institute of Technology Karnataka, Surathkal Dakshina Kannada?
This B.Tech Artificial Intelligence and Machine Learning program at National Institute of Technology Karnataka focuses on developing professionals equipped with cutting-edge skills in AI, ML, and data science. Recognizing India''''s rapidly expanding tech industry, this specialization stands out by integrating theoretical foundations with practical applications, preparing students for innovative roles. It addresses the growing demand for AI expertise across various sectors in the Indian market, reflecting a forward-thinking curriculum.
Who Should Apply?
This program is ideal for fresh graduates seeking entry into the booming fields of artificial intelligence and machine learning. It also caters to working professionals aiming to upskill and integrate AI into their existing domains, or career changers transitioning into the AI industry. Candidates typically possess a strong foundation in mathematics, logical reasoning, and a keen interest in problem-solving through computational methods, making them suitable for this rigorous program.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including AI Engineer, Machine Learning Scientist, Data Scientist, and AI Consultant. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning significantly more based on skill and company. The program fosters continuous growth trajectories in Indian IT giants, startups, and research institutions, often aligning with professional certifications like AWS ML Specialist or Google AI Engineer, enhancing employability.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice problem-solving using languages like C/C++ or Python. Focus on understanding data structures and algorithms thoroughly, as they form the bedrock for advanced AI concepts. Participate in coding competitions to hone logical and coding skills.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks, NPTEL courses on Data Structures
Career Connection
Strong foundational programming skills are essential for clearing technical interviews at product-based companies and efficiently implementing complex AI algorithms later.
Develop Strong Mathematical & Statistical Base- (Semester 1-2)
Pay close attention to Engineering Mathematics, Probability, and Statistics courses. Utilize online resources and textbooks to deepen understanding of linear algebra, calculus, and statistical inference, which are crucial for comprehending and innovating in machine learning.
Tools & Resources
Khan Academy, NPTEL lectures on Probability and Linear Algebra, MIT OpenCourseWare
Career Connection
A robust mathematical background is indispensable for understanding, implementing, and optimizing AI and ML models, differentiating candidates in research and development roles.
Engage in Peer Learning & Collaborative Projects- (Semester 1-2)
Form study groups and actively engage in discussions with peers to clarify concepts. Work on small, collaborative projects or assignments to learn from different perspectives and develop teamwork skills, highly valued in the industry.
Tools & Resources
GitHub for version control, Collaborative online IDEs, Departmental project fairs
Career Connection
Teamwork, communication, and problem-solving skills developed collaboratively are vital for working effectively in interdisciplinary AI teams in corporate or research environments.
Intermediate Stage
Build Practical Data Science & ML Skills- (Semester 3-5)
Translate theoretical knowledge from Machine Learning and DBMS courses into practical projects. Focus on data cleaning, feature engineering, model training, and evaluation using real-world datasets. Participate in college-level hackathons and competitions.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebooks, Sci-kit Learn, Pandas, NumPy, MySQL/PostgreSQL
Career Connection
Directly prepares students for roles like Junior Data Scientist or ML Engineer by providing a strong portfolio of practical applications and hands-on experience.
Seek Early Industry Exposure through Internships- (Semester 4-5)
Actively look for summer internships or part-time projects in AI/ML startups or established companies. This provides invaluable hands-on experience, networking opportunities, and a clear understanding of industry challenges and workflows.
Tools & Resources
LinkedIn, College placement cell, Internshala.com, Company career pages, NITK alumni network
Career Connection
Internships are critical for gaining relevant experience, making industry contacts, and often lead to pre-placement offers, significantly boosting career prospects in India''''s competitive job market.
Specialize in Key AI Domains- (Semester 5)
Begin exploring specialized areas within AI like Natural Language Processing, Computer Vision, or Internet of Things through department electives. Dive deeper into these subjects by reading research papers and undertaking mini-projects in your chosen area of interest.
Tools & Resources
arXiv, Google Scholar, Specific libraries like OpenCV (Computer Vision), NLTK/SpaCy (NLP), DeepLearning.AI courses
Career Connection
Developing expertise in a niche AI domain makes you a more attractive candidate for specialized roles and future research opportunities within the rapidly evolving AI landscape.
Advanced Stage
Engage in Research & Major Projects- (Semester 6-8)
Undertake significant research-oriented major projects, potentially collaborating with faculty or industry experts. Aim for publishable work or innovative solutions to complex real-world problems, demonstrating deep understanding and application of AI/ML principles.
Tools & Resources
TensorFlow, PyTorch, Cloud platforms (AWS, Azure, GCP), Institutional research labs and faculty mentorship
Career Connection
Strong project work and research output are crucial for securing advanced roles, postgraduate studies, and showcasing innovation to potential employers and academic institutions.
Prepare for Placements & Advanced Studies- (Semester 7-8)
Focus on comprehensive preparation for campus placements, including aptitude tests, technical interviews, and HR rounds. Simultaneously, if pursuing higher education, prepare for competitive exams like GATE, GRE, or explore specific AI/ML masters programs globally or in India.
Tools & Resources
Mock interview platforms, Previous year question papers, Placement training modules by college, Career counseling services
Career Connection
This stage directly culminates in securing desirable job placements in top companies or admission to prestigious universities for advanced studies in AI/ML, shaping your long-term career path.
Develop Leadership and Communication Skills- (Semester 7-8)
Take leadership roles in student organizations, technical clubs, or project teams. Practice presenting complex technical topics clearly and concisely to diverse audiences, which is vital for professional growth and effective collaboration.
Tools & Resources
Toastmasters International, University presentation workshops, Leadership development programs, Public speaking clubs
Career Connection
Leadership and effective communication are paramount for career progression into managerial, consulting, or lead AI engineer roles in the Indian corporate landscape, fostering holistic professional development.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 8 semesters
Credits: 180.5 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA110 | Engineering Mathematics - I | Core | 4 | Differential Calculus, Integral Calculus, Ordinary Differential Equations, Laplace Transforms, Vector Calculus |
| PH110 | Engineering Physics | Core | 4 | Modern Physics, Quantum Mechanics, Solid State Physics, Lasers and Fiber Optics, Nanomaterials |
| CY110 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion, Water Technology, Fuels and Combustion, Polymers, Environmental Chemistry |
| CV110 | Basic Civil Engineering | Core | 3 | Introduction to Civil Engineering, Building Materials, Surveying, Transportation Engineering, Water Resources |
| ME110 | Basic Mechanical Engineering | Core | 3 | Thermodynamics, IC Engines, Refrigeration, Power Transmission, Manufacturing Processes |
| CS110 | Problem Solving and Programming | Core | 4 | C Programming Fundamentals, Data Types and Operators, Control Structures, Functions and Arrays, Pointers and Structures, File I/O |
| GE110 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Introduction to AutoCAD |
| GE11L | General Engineering Lab | Lab | 1.5 | Workshop Practice, Carpentry, Welding, Foundry, Fitting |
| PH11L | Engineering Physics Lab | Lab | 1.5 | Experiments on Optics, Electricity and Magnetism, Modern Physics Phenomena, Semiconductor Devices |
| CY11L | Engineering Chemistry Lab | Lab | 1.5 | Volumetric Analysis, pH-metry, Conductometry, Colorimetry, Water Quality Testing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA111 | Engineering Mathematics - II | Core | 4 | Linear Algebra, Vector Spaces, Eigenvalues and Eigenvectors, Numerical Methods, Partial Differential Equations |
| CS111 | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| EC110 | Basic Electronics | Core | 4 | Diode Characteristics, Transistors and Amplifiers, Rectifiers and Filters, Digital Logic Gates, Introduction to Microcontrollers |
| EE110 | Basic Electrical Engineering | Core | 4 | DC Circuits, AC Circuits, Transformers, Motors and Generators, Basic Power Systems |
| BT110 | Basic Biology for Engineers | Core | 3 | Cell Biology, Biomolecules, Genetics, Biotechnology Applications, Environmental Biology |
| HM110 | Technical Communication | Core | 3 | Technical Writing Skills, Oral Presentation Techniques, Group Discussions, Resume Building, Interview Skills |
| CS11L | Programming Lab | Lab | 1.5 | C/C++ Programming Exercises, Implementation of Data Structures, Algorithm Debugging, Problem Solving through Code |
| EC11L | Basic Electronics Lab | Lab | 1.5 | Diode Characteristics Experiments, Transistor Amplifier Circuits, Digital Logic Gates Testing, Basic Electronic Components |
| EE11L | Basic Electrical Engineering Lab | Lab | 1.5 | Verification of Circuit Laws, AC Circuit Analysis, Motor and Generator Characteristics, Electrical Measurements |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI210 | Discrete Mathematics | Core | 4 | Mathematical Logic, Set Theory and Relations, Combinatorics, Graph Theory, Algebraic Structures |
| AI211 | Data Structures and Algorithms | Core | 4 | Recursion, Advanced Trees (AVL, Red-Black), Hashing Techniques, Heaps and Priority Queues, Graph Algorithms |
| AI212 | Digital Logic and Computer Organization | Core | 4 | Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits, Processor Organization, Memory Hierarchy |
| AI213 | Object Oriented Programming | Core | 4 | Classes and Objects, Inheritance and Polymorphism, Abstraction and Encapsulation, Exception Handling, Templates and Generics |
| AI214 | Probability and Statistics for AI | Core | 4 | Probability Theory, Random Variables and Distributions, Hypothesis Testing, Regression Analysis, Correlation and Covariance |
| AI21L | Object Oriented Programming Lab | Lab | 1.5 | C++ / Java Programming, Object-oriented Design Patterns, Data Structure Implementation using OOP, Debugging and Testing |
| AI21R | Research Methodology and IPR | Core | 1.5 | Research Design, Data Collection and Analysis, Report Writing, Intellectual Property Rights (IPR), Ethics in Research |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI220 | Operating Systems | Core | 4 | Process Management, Memory Management, File Systems, I/O Management, Deadlocks and Concurrency |
| AI221 | Database Management Systems | Core | 4 | ER Model and Relational Model, SQL Queries, Normalization, Transaction Management, Concurrency Control and Recovery |
| AI222 | Design and Analysis of Algorithms | Core | 4 | Asymptotic Notations, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms, NP-Completeness |
| AI223 | Theory of Computation | Core | 4 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Decidability and Undecidability |
| AI224 | Introduction to Artificial Intelligence | Core | 4 | Problem Solving through Search, Knowledge Representation, Logical Reasoning, Planning, Machine Learning Basics, Expert Systems |
| AI22L | Operating Systems Lab | Lab | 1.5 | Linux Commands and Shell Scripting, Process Synchronization, Memory Allocation Algorithms, File System Operations |
| AI22M | Database Management Systems Lab | Lab | 1.5 | SQL Queries (DDL, DML, DCL), PL/SQL Programming, Database Design Implementation, Transaction Control |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI310 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning Basics, Regression and Classification Models, Clustering Techniques, Model Evaluation |
| AI311 | Computer Networks | Core | 4 | OSI and TCP/IP Models, Data Link Layer Protocols, Network Layer Protocols (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols |
| AI312 | Software Engineering | Core | 4 | Software Life Cycle Models, Requirements Engineering, Software Design Principles, Software Testing, Project Management, Agile Methodologies |
| AI313 | Optimization Techniques | Core | 4 | Linear Programming, Simplex Method, Duality Theory, Transportation Problems, Assignment Problems, Non-linear Programming |
| AI314 | Formal Languages and Automata Theory | Elective | 3 | Chomsky Hierarchy, Regular Grammars, Context-Free Grammars, Parsing Techniques, Turing Machines |
| AI315 | Advanced Data Structures | Elective | 3 | B-Trees and Tries, Skip Lists, Amortized Analysis, Suffix Arrays and Trees, Dynamic Connectivity |
| AI316 | Digital Image Processing | Elective | 3 | Image Fundamentals, Image Enhancement, Image Restoration, Image Compression, Image Segmentation |
| AI317 | Natural Language Processing | Elective | 3 | Text Preprocessing, N-grams and Language Models, Part-of-Speech Tagging, Parsing and Syntax, Machine Translation Basics |
| AI318 | Internet of Things | Elective | 3 | IoT Architecture, Sensors and Actuators, Communication Protocols (MQTT, CoAP), Cloud Platforms for IoT, IoT Security and Privacy |
| AI319 | Computer Graphics | Elective | 3 | Graphics Primitives, 2D and 3D Transformations, Viewing and Clipping, Shading and Lighting Models, Rendering Techniques |
| AI31L | Machine Learning Lab | Lab | 1.5 | Python for ML (Scikit-learn, Pandas), Regression and Classification Implementations, Clustering Algorithms, Model Hyperparameter Tuning, Data Preprocessing for ML |
| AI31P | Minor Project - I | Project | 1.5 | Problem Identification, Literature Survey, System Design, Implementation and Testing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI320 | Deep Learning | Core | 4 | Artificial Neural Networks, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Models (GANs, VAEs), Deep Learning Frameworks (TensorFlow, PyTorch) |
| AI321 | Big Data Analytics | Core | 4 | Big Data Technologies, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark, NoSQL Databases, Data Warehousing, Data Visualization |
| AI322 | Reinforcement Learning | Core | 4 | Markov Decision Processes (MDPs), Dynamic Programming, Monte Carlo Methods, Q-learning and SARSA, Policy Gradient Methods, Deep Reinforcement Learning |
| AI324 | Computer Vision | Elective | 3 | Image Formation and Filtering, Feature Detection and Matching, Object Recognition, Image Segmentation, Motion Analysis |
| AI325 | Speech and Audio Processing | Elective | 3 | Speech Production and Perception, Acoustic Phonetics, Speech Recognition Systems, Speech Synthesis, Audio Feature Extraction |
| AI326 | Robotics | Elective | 3 | Robot Kinematics and Dynamics, Robot Sensing, Robot Actuators, Robot Control, Path Planning |
| AI327 | Blockchain Technology | Elective | 3 | Cryptography Fundamentals, Distributed Ledgers, Consensus Mechanisms, Smart Contracts, Cryptocurrency Principles |
| AI328 | Quantum Computing | Elective | 3 | Quantum Mechanics Basics, Qubits and Quantum Gates, Quantum Algorithms (Shor''''s, Grover''''s), Quantum Error Correction, Quantum Supremacy |
| AI329 | Ethical AI | Elective | 3 | AI Bias and Fairness, Accountability and Transparency, Privacy in AI Systems, Societal Impact of AI, AI Regulations and Governance |
| AI32L | Deep Learning Lab | Lab | 1.5 | TensorFlow/PyTorch Implementation, CNNs for Image Recognition, RNNs for Sequence Data, Transfer Learning, Generative Model Training |
| AI32M | Big Data Analytics Lab | Lab | 1.5 | Hadoop HDFS Operations, MapReduce Programming, Apache Spark Applications, Hive and Pig Scripting, NoSQL Database Interaction |
| AI32P | Minor Project - II | Project | 1.5 | Advanced Project Development, System Integration, Testing and Debugging, Technical Documentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI410 | Applied AI and Cognitive Systems | Core | 4 | Knowledge Engineering, Expert Systems, Cognitive Architectures, Reasoning under Uncertainty, Multi-agent Systems |
| HM410 | Entrepreneurship and Management | Core | 3 | Entrepreneurial Process, Business Plan Development, Marketing Strategies, Financial Management, Human Resource Management, Legal Aspects of Business |
| AI411 | Explainable AI | Elective | 3 | Interpretability and Explainability, Local and Global Explanations, LIME and SHAP Methods, Causal Inference in AI, Fairness and Transparency in AI |
| AI412 | Generative AI Models | Elective | 3 | Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, Large Language Models (LLMs), Generative Image/Text Synthesis |
| AI413 | Human Computer Interaction | Elective | 3 | Usability Principles, User Experience (UX) Design, Prototyping and Wireframing, Evaluation Techniques (Heuristic, User Testing), Accessibility Design |
| AI414 | GPU Computing | Elective | 3 | Parallel Computing Concepts, CUDA Architecture, GPU Programming Models, Memory Optimization for GPU, Performance Tuning for CUDA |
| AI415 | Graph Neural Networks | Elective | 3 | Graph Theory Fundamentals, Graph Embeddings, Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Applications of GNNs |
| AI416 | Swarm Intelligence | Elective | 3 | Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithms, Evolutionary Computing, Collective Behavior Modeling |
| AI417 | Federated Learning | Elective | 3 | Privacy-preserving Machine Learning, Decentralized Learning, Client-Server Aggregation, Secure Aggregation Protocols, Differential Privacy in FL |
| AI418 | Time Series Analysis and Forecasting | Elective | 3 | Stationarity and Autocorrelation, ARIMA Models, Exponential Smoothing Methods, State-Space Models, Deep Learning for Time Series |
| AI419 | Game Theory for AI | Elective | 3 | Strategic Games, Nash Equilibrium, Extensive Form Games, Cooperative Game Theory, Reinforcement Learning and Game Theory |
| AI41P | Major Project - I | Project | 3 | Comprehensive Project Design, System Architecture, Advanced Implementation, Testing and Validation, Mid-term Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI421 | AI in Healthcare | Elective | 3 | Medical Imaging Analysis, Drug Discovery and Development, Clinical Decision Support Systems, Electronic Health Records (EHR) Analytics, Personalized Medicine |
| AI422 | AI in Finance | Elective | 3 | Algorithmic Trading, Fraud Detection, Risk Management, Robo-advisors, Financial Forecasting and Sentiment Analysis |
| AI423 | AI in Education | Elective | 3 | Personalized Learning Systems, Intelligent Tutoring Systems, Learning Analytics, Automated Assessment, Adaptive Learning Platforms |
| AI424 | AI in Cyber Security | Elective | 3 | Intrusion Detection Systems, Malware Analysis, Anomaly Detection, Threat Intelligence, Blockchain Security |
| AI425 | AI in Robotics and Automation | Elective | 3 | Robotic Process Automation (RPA), Industrial Robots, Human-Robot Interaction, Autonomous Systems, Robot Path Planning |
| AI426 | AI in Supply Chain Management | Elective | 3 | Demand Forecasting, Inventory Optimization, Logistics and Routing, Predictive Maintenance, Supply Chain Risk Management |
| AI427 | AI in Agriculture | Elective | 3 | Precision Agriculture, Crop Monitoring and Yield Prediction, Disease and Pest Detection, Smart Irrigation, Livestock Management |
| AI428 | Natural Computing | Elective | 3 | Bio-inspired Algorithms, Evolutionary Computation, Neural Networks, Swarm Intelligence, DNA Computing |
| AI429 | Intelligent Agents | Elective | 3 | Agent Architectures, Rational Agents, Multi-agent Systems, Game Theory in Agents, Distributed Artificial Intelligence |
| AI42P | Major Project - II | Project | 9 | Advanced Project Development, Research Contribution, System Validation and Evaluation, Publication Readiness, Final Thesis/Report Submission |
