.png&w=1920&q=75)
B-TECH in Name Artificial Intelligence And Machine Learning Seats Na Average Tuition Fee 1 25 000 Per Year at National Institute of Technology Sikkim
South Sikkim, Sikkim
.png&w=1920&q=75)
About the Specialization
What is {"name": "Artificial Intelligence and Machine Learning", "seats": "NA", "average_tuition_fee": "₹1,25,000 per year"} at National Institute of Technology Sikkim South Sikkim?
This Artificial Intelligence and Machine Learning program at National Institute of Technology Sikkim focuses on equipping students with deep knowledge and practical skills in AI, ML, and Data Science. It addresses the rapidly growing demand for skilled professionals in the Indian technology sector, emphasizing foundational concepts alongside cutting-edge applications relevant to industries like IT, healthcare, finance, and automotive. The curriculum is designed to foster innovation and problem-solving abilities.
Who Should Apply?
This program is ideal for aspiring engineers passionate about developing intelligent systems and data-driven solutions. It attracts fresh graduates with a strong mathematical and computational aptitude, eager to delve into advanced algorithms and their applications. Working professionals seeking to transition into the booming AI/ML domain or upskill for leadership roles in Indian tech firms will also find the comprehensive curriculum beneficial, alongside career changers aiming for high-impact roles.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding India-specific career paths as AI Engineers, Machine Learning Scientists, Data Analysts, or NLP Specialists in leading companies and startups. Entry-level salaries typically range from ₹5-8 LPA, with experienced professionals earning ₹15-30+ LPA, reflecting strong growth trajectories. The skills acquired align with global certifications and prepare students for impactful contributions to India''''s digital transformation.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate time to strengthen C/C++/Python programming skills through daily coding challenges on platforms like HackerRank and LeetCode. A solid foundation in programming and data structures is crucial for subsequent AI/ML courses.
Tools & Resources
CodeChef, GeeksforGeeks, HackerRank, LeetCode
Career Connection
This enhances problem-solving abilities, directly impacting placement readiness for technical interviews and coding rounds.
Form Study Groups- (Semester 1-2)
Engage in collaborative learning with peers, discussing complex mathematical and programming concepts. Peer-to-peer teaching clarifies doubts, reinforces understanding, and builds a supportive academic community.
Tools & Resources
Online collaboration tools, Campus study spaces
Career Connection
Regularly reviewing notes and solving problems together prepares students for competitive exams and advanced coursework, fostering teamwork skills valued in industry.
Explore Basic AI Concepts- (Semester 1-2)
Begin exploring introductory AI/ML concepts through online courses from NPTEL or Coursera, such as Andrew Ng''''s Machine Learning course. Early exposure builds interest and provides a head start.
Tools & Resources
NPTEL, Coursera, edX, Kaggle Learn
Career Connection
This makes advanced topics easier to grasp and connects theoretical knowledge to real-world applications, informing future specialization choices.
Intermediate Stage
Undertake Mini-Projects & Kaggle Competitions- (Semester 3-5)
Apply theoretical knowledge by working on mini-projects related to Foundations of AI, Machine Learning, and Deep Learning. Participate in Kaggle competitions or build personal projects from real-world datasets.
Tools & Resources
Kaggle, GitHub, Google Colab, Jupyter Notebooks
Career Connection
This practical experience is invaluable for building a robust portfolio and understanding industry problem statements, essential for interviews and job roles.
Seek Industry Internships- (Semester 3-5)
Actively pursue summer internships with Indian tech companies, startups, or research labs focused on AI/ML. Internships provide invaluable hands-on experience and networking opportunities.
Tools & Resources
LinkedIn, Internshala, College Placement Cell
Career Connection
Internships offer a glimpse into corporate culture and significantly boost resume credibility for future placements, often leading to pre-placement offers.
Specialize with Electives- (Semester 3-5)
Strategically choose professional and open electives that align with personal career interests, such as NLP, Computer Vision, or Big Data. Deep diving into a specialized area enhances expertise.
Tools & Resources
Syllabus document, Faculty advisors, Industry trend reports
Career Connection
Specialized knowledge differentiates students during job applications and enables them to target specific high-demand roles in cutting-edge AI/ML domains.
Advanced Stage
Develop a Capstone/Major Project- (Semester 6-8)
Focus on a significant, real-world AI/ML project that showcases advanced skills and problem-solving capabilities, collaborating with faculty or industry mentors. Aim for innovative solutions.
Tools & Resources
Research papers, Cloud platforms AWS, Azure, GCP, Advanced ML frameworks
Career Connection
A strong major project is often a key determinant in securing top placements and demonstrates readiness for complex engineering challenges in the AI/ML industry.
Intensive Placement Preparation- (Semester 6-8)
Dedicate time to rigorous preparation for technical interviews, including advanced data structures and algorithms, system design, and AI/ML specific questions. Practice mock interviews and aptitude tests.
Tools & Resources
InterviewBit, GeeksforGeeks, LeetCode, Mock interview platforms
Career Connection
This structured approach optimizes chances for high-paying positions in leading Indian and multinational tech companies that recruit AI/ML talent.
Network and Professional Engagement- (Semester 6-8)
Attend industry seminars, workshops, and AI/ML conferences in India, such as Cypher or GIDS. Connect with professionals and alumni on platforms like LinkedIn to build a strong network.
Tools & Resources
LinkedIn, Conference websites, Professional bodies IET, IEEE
Career Connection
Networking opens doors to job opportunities, mentorship, and helps in staying updated on industry trends, crucial for long-term career growth in the dynamic tech landscape.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 with Physics, Chemistry, and Mathematics; JEE (Main) score and rank for JoSAA/CSAB counseling.
Duration: 8 semesters/ 4 years
Credits: 185 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA1001 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Ordinary Differential Equations, Laplace Transforms |
| PH1001 | Engineering Physics | Core | 4 | Wave Optics, Quantum Mechanics, Solid State Physics, Lasers, Fiber Optics |
| EE1001 | Basic Electrical Engineering | Core | 4 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines |
| CS1001 | Problem Solving and Programming | Core | 3 | Programming Fundamentals, Conditional Statements, Looping Constructs, Functions, Arrays, Pointers, File I/O |
| HS1001 | English for Communication | Core | 2 | Grammar and Vocabulary, Reading Comprehension, Written Communication, Oral Communication, Presentation Skills |
| ME1001 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, CAD Basics, Development of Surfaces |
| PH1002 | Engineering Physics Lab | Lab | 2 | Experiments on Optics, Electricity and Magnetism, Modern Physics principles, Semiconductor device characteristics |
| EE1002 | Basic Electrical Engineering Lab | Lab | 2 | Verification of Circuit Laws, Measurement of Electrical Quantities, Characteristics of Devices, Wiring and safety practices |
| CS1002 | Problem Solving and Programming Lab | Lab | 2 | Problem solving using C language, Data manipulation and control structures, Function implementation and modular programming, Debugging and testing |
| ME1002 | Workshop Practice Lab | Lab | 2 | Carpentry tools and joints, Fitting operations and accuracy, Welding techniques, Machining processes, Foundry practice |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA1002 | Engineering Mathematics-II | Core | 4 | Sequences and Series, Vector Calculus, Complex Numbers, Probability basics |
| CY1001 | Engineering Chemistry | Core | 4 | Water Technology, Fuels and Combustion, Corrosion and its control, Polymers and their properties, Spectroscopic Techniques |
| EC1001 | Basic Electronics Engineering | Core | 4 | Semiconductor Devices, Diodes and their applications, Transistors BJT, FET, Rectifiers and Power Supplies, Amplifiers basic configurations |
| CS1003 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Traversal, Sorting and Searching Algorithms |
| EV1001 | Environmental Science | Core | 2 | Ecosystems and Energy Flow, Biodiversity and Conservation, Environmental Pollution, Renewable Energy Sources, Environmental Legislation and Ethics |
| HS1002 | Professional Communication | Core | 2 | Technical Report Writing, Presentation Skills, Group Discussion Techniques, Interview Skills, Interpersonal Communication |
| CY1002 | Engineering Chemistry Lab | Lab | 2 | Quantitative Analysis methods, Water Quality Testing, Polymer Synthesis and Characterization, Instrumental analysis techniques |
| EC1002 | Basic Electronics Engineering Lab | Lab | 2 | Characteristics of Diodes and Zener Diodes, Transistor characteristics, Rectifiers and filter circuits, Amplifier design and testing |
| CS1004 | Data Structures Lab | Lab | 2 | Implementation of arrays and linked lists, Stack and Queue applications, Tree and graph traversal algorithms, Sorting and searching program development |
| CE1001 | Engineering Mechanics | Core | 4 | Forces and Equilibrium, Moments and Couples, Kinematics of Particles and Rigid Bodies, Dynamics of Particles, Friction and Simple Machines |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA2001 | Discrete Mathematics | Core | 4 | Set Theory and Logic, Relations and Functions, Graph Theory, Combinatorics, Recurrence Relations |
| CS2001 | Object Oriented Programming | Core | 3 | Classes and Objects, Inheritance and Polymorphism, Abstraction and Encapsulation, Exception Handling, File I/O and Templates |
| CS2002 | Computer Organization and Architecture | Core | 4 | CPU Organization, Memory Hierarchy, I/O Organization, Instruction Sets, Pipelining and Parallelism |
| EC2001 | Digital Logic Design | Core | 4 | Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits, Registers and Counters, Memory elements |
| AI2001 | Foundations of Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms Heuristic and Uninformed, Knowledge Representation and Reasoning, Game Playing and Adversarial Search |
| AI2002 | Linear Algebra and Calculus for AI/ML | Core | 4 | Vector Spaces and Matrices, Eigenvalues and Eigenvectors, Differentiation and Partial Derivatives, Optimization Techniques, Gradient Descent Algorithms |
| CS2003 | Object Oriented Programming Lab | Lab | 2 | Practical implementation of OOP concepts, Class and object design, Inheritance and polymorphism exercises, Error handling and debugging |
| EC2002 | Digital Logic Design Lab | Lab | 2 | Design and implementation of logic gates, Combinational circuit experiments, Sequential circuit design, FPGA/CPLD programming basics |
| AI2003 | AI Project-I | Project | 1 | Basic AI project development, Problem formulation and analysis, Implementation of simple AI algorithms, Project report and presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA2002 | Probability and Statistics | Core | 4 | Probability Distributions, Hypothesis Testing, Regression and Correlation Analysis, Sampling Theory, ANOVA |
| CS2004 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems and Device Management, Deadlocks and Concurrency Control |
| CS2005 | Database Management Systems | Core | 3 | Relational Model, SQL Query Language, ER Diagrams and Database Design, Normalization, Transaction Management and Concurrency |
| AI2004 | Machine Learning | Core | 3 | Supervised Learning Regression and Classification, Unsupervised Learning Clustering, Model Evaluation and Validation, Ensemble Methods, Support Vector Machines |
| AI2005 | Data Structures and Algorithms for AI/ML | Core | 3 | Advanced Data Structures Heaps, Tries, Algorithm Design Paradigms, Complexity Analysis, Heuristic Search Algorithms, Dynamic Programming |
| AI2006 | Natural Language Processing | Core | 3 | Text Preprocessing Tokenization, Stemming, N-grams and Language Models, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation Fundamentals |
| HS2001 | Professional Ethics and Human Values | Core | 2 | Ethical Theories and Principles, Professionalism in Engineering, Cyber Ethics and Data Privacy, Environmental Ethics, Human Values and Corporate Social Responsibility |
| CS2006 | Operating Systems Lab | Lab | 2 | Shell scripting and basic commands, Process synchronization problems, Memory allocation strategies, File system calls and operations |
| CS2007 | Database Management Systems Lab | Lab | 2 | SQL queries DDL, DML, DCL, Database schema design and implementation, Transaction control statements, Working with views and stored procedures |
| AI2007 | Machine Learning Lab | Lab | 2 | Implementation of ML algorithms using Python, Data preprocessing and feature engineering, Model training and evaluation techniques, Use of Scikit-learn and other ML libraries |
| AI2008 | AI Project-II | Project | 1 | Intermediate AI project development, Application of ML algorithms to real-world problems, Data collection and analysis, Project documentation and presentation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI3001 | Deep Learning | Core | 3 | Neural Networks Fundamentals, Activation Functions and Backpropagation, Convolutional Neural Networks CNNs, Recurrent Neural Networks RNNs, Transformers and Attention Mechanisms |
| AI3002 | Data Analytics | Core | 3 | Data Collection and Cleaning, Data Transformation and Aggregation, Exploratory Data Analysis EDA, Statistical Inference and Hypothesis Testing, Data Visualization Techniques |
| AI3003 | Computer Vision | Core | 3 | Image Processing Fundamentals, Feature Extraction and Matching, Object Detection Techniques, Image Segmentation, Facial Recognition Systems |
| OE-I | Open Elective I | Elective | 3 | Topics depend on the chosen Open Elective from the approved list. |
| PE-I | Professional Elective I | Elective | 3 | Topics depend on the chosen Professional Elective from the approved list. |
| HS3001 | Managerial Economics | Core | 2 | Demand and Supply Analysis, Production Theory, Market Structures and Pricing Strategies, Cost Analysis, Capital Budgeting Decisions |
| AI3004 | Deep Learning Lab | Lab | 2 | Implementation of deep learning models, Use of frameworks like TensorFlow and PyTorch, Training and fine-tuning neural networks, Application to image and sequence data |
| AI3005 | Data Analytics Lab | Lab | 2 | Practical data cleaning and preprocessing, Statistical modeling and hypothesis testing, Data visualization using tools like Matplotlib, Seaborn, Building analytical dashboards |
| AI3006 | Computer Vision Lab | Lab | 2 | Image processing tasks using OpenCV, Object detection model implementation, Image segmentation techniques, Face detection and recognition applications |
| AI3007 | AI Project-III | Project | 1 | Advanced AI project development, Deep learning applications, Model deployment considerations, Performance evaluation and optimization |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI3008 | Reinforcement Learning | Core | 3 | Markov Decision Processes MDPs, Q-learning and SARSA, Policy Gradient Methods, Deep Reinforcement Learning, Exploration vs Exploitation |
| AI3009 | Big Data Analytics | Core | 3 | Hadoop Ecosystem, Apache Spark for Big Data Processing, NoSQL Databases, Distributed Computing Architectures, Real-time Data Streaming |
| AI3010 | Artificial Neural Networks | Core | 3 | Perceptrons and Multilayer Perceptrons, Radial Basis Function Networks, Self-Organizing Maps SOMs, Hopfield Networks, Boltzmann Machines |
| OE-II | Open Elective II | Elective | 3 | Topics depend on the chosen Open Elective from the approved list. |
| PE-II | Professional Elective II | Elective | 3 | Topics depend on the chosen Professional Elective from the approved list. |
| HS3002 | Industrial Management | Core | 2 | Principles of Management, Production and Operations Management, Marketing Management, Financial Management basics, Human Resource Management |
| AI3011 | Reinforcement Learning Lab | Lab | 2 | Implementation of RL algorithms Q-learning, SARSA, Agent training in simulated environments, Policy optimization techniques, Application to game environments |
| AI3012 | Big Data Analytics Lab | Lab | 2 | Hands-on with Hadoop MapReduce framework, Spark programming for data processing, Working with distributed file systems HDFS, Implementing data pipelines |
| AI3013 | AI Project-IV | Project | 1 | Specialization project in a chosen AI/ML domain, Research-oriented problem solving, Literature review and methodology design, Interim report and presentation |
| AI3014 | Internship | Core | 2 | Practical industry experience in AI/ML, Application of theoretical knowledge in real-world settings, Professional skill development, Industry problem solving and report generation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PE-III | Professional Elective III | Elective | 3 | Topics depend on the chosen Professional Elective from the approved list. |
| PE-IV | Professional Elective IV | Elective | 3 | Topics depend on the chosen Professional Elective from the approved list. |
| OE-III | Open Elective III | Elective | 3 | Topics depend on the chosen Open Elective from the approved list. |
| AI4001 | AI Project-V | Project | 3 | Major project phase 1 advanced research, System design and architecture, Methodology development and implementation plan, Initial results and progress report |
| AI4002 | AI Seminar | Core | 1 | Presentation on advanced AI topics, Research paper analysis and critique, Technical communication skills, Current trends in AI/ML |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PE-V | Professional Elective V | Elective | 3 | Topics depend on the chosen Professional Elective from the approved list. |
| PE-VI | Professional Elective VI | Elective | 3 | Topics depend on the chosen Professional Elective from the approved list. |
| AI4003 | Major Project | Project | 6 | Final project development and implementation, Testing, validation, and optimization, Comprehensive documentation and technical report, Final presentation and demonstration of the AI/ML system |
