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M-TECH in Name Artificial Intelligence And Machine Learning Seats 25 Average Tuition Fee 70 000 Per Year at National Institute of Technology Sikkim
South Sikkim, Sikkim
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About the Specialization
What is {"name": "Artificial Intelligence and Machine Learning", "seats": 25, "average_tuition_fee": "₹70,000 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 advanced concepts and practical applications of AI and ML. It is designed to equip students with a deep understanding of intelligent systems, data analysis, and predictive modeling, catering to the growing demand for AI specialists across various Indian industries. The program emphasizes both theoretical foundations and hands-on project experience.
Who Should Apply?
This program is ideal for engineering graduates, especially from Computer Science, IT, or related fields, who possess a strong aptitude for mathematics and programming. It also welcomes working professionals seeking to upskill in cutting-edge AI technologies or career changers aiming to transition into the rapidly evolving AI industry. A valid GATE score is a prerequisite for admission.
Why Choose This Course?
Graduates of this program can expect to pursue dynamic career paths as AI Engineers, Machine Learning Scientists, Data Scientists, or Research Associates in India''''s booming tech sector. Entry-level salaries typically range from ₹6-10 LPA, growing significantly with experience. The curriculum prepares students for roles in startups, MNCs, and R&D institutions, contributing to India''''s digital transformation.
Student Success Practices
Foundation Stage
Strengthen Mathematical & Algorithmic Foundations- (Semester 1-2)
Dedicate time to master linear algebra, calculus, probability, and advanced data structures. These are the bedrock of AI/ML. Practice problem-solving rigorously for algorithms and competitive programming challenges.
Tools & Resources
NPTEL courses on Linear Algebra/Probability, GeeksforGeeks, HackerRank for algorithms
Career Connection
A strong foundation enhances understanding of complex ML algorithms and is crucial for cracking technical interviews for AI/ML roles.
Build a Robust Programming Portfolio- (Semester 1-2)
Actively apply theoretical knowledge by implementing algorithms and models in Python. Develop small projects demonstrating proficiency in libraries like NumPy, Pandas, Scikit-learn, and initial deep learning frameworks.
Tools & Resources
Jupyter Notebook, VS Code, GitHub, Kaggle introductory datasets
Career Connection
A well-documented GitHub portfolio showcasing practical skills is invaluable for securing internships and placements, demonstrating hands-on expertise.
Engage in Peer Learning and Discussion Groups- (Semester 1-2)
Form study groups with peers to discuss complex topics, solve problems collaboratively, and clarify doubts. Explaining concepts to others solidifies your own understanding and builds teamwork skills.
Tools & Resources
WhatsApp/Telegram groups, Discord servers for AI/ML discussions, Shared Google Docs
Career Connection
Effective collaboration is a critical skill in industry, and peer learning fosters this. It also expands your professional network early on.
Intermediate Stage
Undertake Practical Mini-Projects & Internships- (Semester 3-4)
Beyond coursework, identify real-world problems and develop mini-projects applying learned AI/ML concepts. Actively seek and complete internships to gain industry exposure and apply theoretical knowledge in a professional setting.
Tools & Resources
Kaggle competitions, Datasets from UCI Machine Learning Repository, Internshala for internship search
Career Connection
Practical experience through projects and internships makes your resume stand out, provides networking opportunities, and often leads to pre-placement offers.
Specialize in a Niche AI/ML Area- (Semester 3-4)
Explore elective subjects to identify an area of deeper interest (e.g., NLP, Computer Vision, Reinforcement Learning). Focus on mastering the concepts and tools specific to your chosen niche through advanced courses and projects.
Tools & Resources
Coursera/edX specialized tracks, Official documentation for PyTorch/TensorFlow, Research papers on arXiv
Career Connection
Specialization makes you a more attractive candidate for specific roles and provides a clear direction for your Master''''s dissertation.
Network and Attend Industry Events- (Semester 3-4)
Connect with faculty, alumni, and industry professionals through workshops, seminars, and conferences. Building a professional network can open doors to mentorship, internships, and future job opportunities.
Tools & Resources
LinkedIn, AI/ML meetups (online/offline), Tech conferences in India
Career Connection
Networking is crucial for understanding industry trends, discovering hidden job opportunities, and gaining insights from experienced professionals.
Advanced Stage
Focus on Dissertation and Research Publication- (Semester 3-4 (throughout Dissertation Parts I & II))
Invest deeply in your Master''''s Dissertation, aiming for high-quality research that can potentially lead to publications in conferences or journals. This demonstrates advanced problem-solving and research capabilities.
Tools & Resources
LaTeX for thesis writing, Academic search engines (Google Scholar), Plagiarism detection tools
Career Connection
A strong dissertation and publications enhance your profile for R&D roles, academic positions, or PhD opportunities, showcasing your ability to contribute original work.
Master Placement-Specific Skills- (Semester 3-4 (especially 4))
Practice coding interview questions, brush up on core CS fundamentals, and prepare for behavioral interviews. Tailor your resume and cover letters for specific AI/ML roles and companies. Participate in mock interviews.
Tools & Resources
LeetCode, Cracking the Coding Interview, Glassdoor for company-specific interview questions
Career Connection
Systematic placement preparation significantly increases your chances of securing a desirable job offer from top companies visiting the campus.
Develop Leadership and Communication Abilities- (Throughout the program)
Participate in student clubs, lead project teams, and hone presentation skills. Effective communication is vital for conveying complex technical concepts to diverse audiences, both technical and non-technical.
Tools & Resources
Toastmasters International (if available), College debate/presentation clubs, Peer review sessions
Career Connection
Strong leadership and communication skills are essential for career progression into senior engineering, team lead, or management roles in the Indian tech industry.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in relevant discipline or Master''''s degree in Science/Mathematics/Statistics/Computer Applications with a valid GATE score. Minimum 6.5 CGPA or 60% marks for GN/EWS/OBC candidates and 6.0 CGPA or 55% marks for SC/ST/PwD candidates. Admissions are conducted through CCMT.
Duration: 4 semesters / 2 years
Credits: 68 Credits
Assessment: Internal: 40% (for theory courses) / 60% (for practical/lab courses), External: 60% (for theory courses) / 40% (for practical/lab courses)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI5101 | Mathematical Foundations for AI and ML | Core | 3 | Linear Algebra for Machine Learning, Calculus and Optimization, Probability Theory and Distributions, Statistical Inference and Hypothesis Testing, Random Processes and Stochastic Models, Graph Theory Fundamentals |
| AI5102 | Advanced Data Structures and Algorithms | Core | 3 | Asymptotic Analysis and Recurrences, Advanced Tree Structures (B-Trees, Red-Black Trees), Graph Algorithms (Flow Networks, Matching), Dynamic Programming Techniques, Greedy Algorithms and Matroids, Randomized Algorithms and Amortized Analysis |
| AI5103 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Reinforcement Learning Basics, Model Evaluation and Hyperparameter Tuning, Ensemble Methods (Bagging, Boosting), Bias-Variance Tradeoff and Regularization |
| AI5104 | Advanced Machine Learning Lab | Lab | 2 | Python for Data Science and ML, Implementation of Supervised Learning Algorithms, Implementation of Unsupervised Learning Algorithms, Introduction to Deep Learning Frameworks (TensorFlow, PyTorch), Model Preprocessing and Feature Engineering, Practical Aspects of Model Deployment |
| AI5105 | Research Methodology | Core | 2 | Research Problem Identification and Formulation, Literature Review Techniques, Research Design and Methods, Data Collection and Sampling, Statistical Analysis for Research, Technical Report Writing and Ethics |
| AIE101 | Elective-I (Choice of one) | Elective Placeholder | 3 | Students choose one from the following options:, AI5111: Advanced Database Management Systems, AI5112: Advanced Operating Systems, AI5113: Advanced Computer Networks |
| AI5111 | Advanced Database Management Systems | Elective Option | 3 | Relational Model and SQL Optimization, Transaction Management and Concurrency Control, Distributed Databases and Architectures, NoSQL Databases (Key-value, Document, Graph), Data Warehousing and OLAP, Big Data Storage Systems |
| AI5112 | Advanced Operating Systems | Elective Option | 3 | Process Management and Scheduling, Memory Management Techniques, Distributed Operating Systems, Real-time Operating Systems, File Systems and I/O Management, Operating System Security |
| AI5113 | Advanced Computer Networks | Elective Option | 3 | Network Architectures and Models, Advanced Routing Protocols, Congestion Control Mechanisms, Wireless and Mobile Networks, Network Security Protocols, Software Defined Networking (SDN) |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI5201 | Deep Learning | Core | 3 | Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Transformers and Attention Mechanisms, Generative Adversarial Networks (GANs), Deep Learning Frameworks and Architectures |
| AI5202 | Natural Language Processing | Core | 3 | Text Preprocessing and Tokenization, Language Models (N-gram, Neural), Syntactic and Semantic Parsing, Named Entity Recognition (NER), Sentiment Analysis and Text Classification, Machine Translation and Text Generation |
| AI5203 | Deep Learning Lab | Lab | 2 | Implementation of CNNs for Image Tasks, Implementation of RNNs for Sequence Tasks, Transfer Learning with Pre-trained Models, Using Hugging Face Transformers Library, Building Generative Models, Deep Learning Project Development |
| AI5204 | Mini Project | Project | 4 | Problem Identification and Scope Definition, Literature Survey and State-of-the-Art Analysis, System Design and Methodology, Implementation and Testing, Report Writing and Presentation, Evaluation and Future Work |
| AIE201 | Elective-II (Choice of one) | Elective Placeholder | 3 | Students choose one from the following options:, AI5211: Computer Vision, AI5212: Reinforcement Learning, AI5213: Big Data Analytics |
| AI5211 | Computer Vision | Elective Option | 3 | Image Formation and Filtering, Feature Detection and Description, Object Detection and Recognition, Image Segmentation and Tracking, Facial Recognition and Biometrics, 3D Computer Vision |
| AI5212 | Reinforcement Learning | Elective Option | 3 | Markov Decision Processes (MDPs), Dynamic Programming (Value, Policy Iteration), Monte Carlo Methods, Q-Learning and SARSA, Policy Gradient Methods, Deep Reinforcement Learning (DQN, DDPG) |
| AI5213 | Big Data Analytics | Elective Option | 3 | Big Data Technologies and Ecosystem, Hadoop Distributed File System (HDFS), MapReduce Programming Model, Apache Spark for Data Processing, NoSQL Databases for Big Data, Data Stream Processing |
| AIE202 | Elective-III (Choice of one) | Elective Placeholder | 3 | Students choose one from the following options:, AI5211: Computer Vision, AI5212: Reinforcement Learning, AI5213: Big Data Analytics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI6101 | Dissertation Part-I | Project | 12 | In-depth Problem Definition and Justification, Comprehensive Literature Review, Development of Research Methodology, Initial Data Collection and Analysis, Preliminary Results and Proof of Concept, Report Writing and Presentation of Progress |
| AIE301 | Elective-IV (Choice of one) | Elective Placeholder | 3 | Students choose one from the following options:, AI6111: Speech and Audio Processing, AI6112: Internet of Things, AI6113: Cloud Computing |
| AI6111 | Speech and Audio Processing | Elective Option | 3 | Speech Production and Perception, Digital Signal Processing for Audio, Feature Extraction for Speech (MFCC, LPC), Automatic Speech Recognition (ASR), Speaker Recognition and Verification, Audio Event Detection and Synthesis |
| AI6112 | Internet of Things | Elective Option | 3 | IoT Architecture and Paradigms, Sensors, Actuators, and Microcontrollers, IoT Communication Protocols (MQTT, CoAP), IoT Cloud Platforms (AWS IoT, Azure IoT), Edge and Fog Computing, IoT Security and Privacy |
| AI6113 | Cloud Computing | Elective Option | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization Technologies, Cloud Security and Data Privacy, Major Cloud Providers (AWS, Azure, GCP) Services, Serverless Computing and FaaS, Containerization (Docker, Kubernetes) |
| AIE302 | Elective-V (Choice of one) | Elective Placeholder | 3 | Students choose one from the following options:, AI6111: Speech and Audio Processing, AI6112: Internet of Things, AI6113: Cloud Computing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI6201 | Dissertation Part-II | Project | 16 | Extensive Implementation and Experimentation, Detailed Results Analysis and Interpretation, Thesis Writing and Documentation, Refinement of Research Contributions, Preparation for Final Dissertation Defense, Potential for Research Publication |
