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M-TECH in Artificial Intelligence at Kalinga Institute of Industrial Technology
Khordha, Odisha
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About the Specialization
What is Artificial Intelligence at Kalinga Institute of Industrial Technology Khordha?
This M.Tech in Computer Science and Engineering with a specialization in Artificial Intelligence and Machine Learning at Kalinga Institute of Industrial Technology focuses on equipping students with advanced theoretical knowledge and practical skills in AI. The program addresses the rapidly evolving Indian industry''''s demand for skilled AI professionals, emphasizing cutting-edge research and real-world application. Its interdisciplinary approach distinguishes it, preparing graduates for complex challenges in the AI landscape.
Who Should Apply?
This program is ideal for engineering graduates with a background in Computer Science, IT, Electronics, or related fields who aspire to build a career in AI. It also caters to working professionals seeking to upskill in advanced AI and ML techniques to drive innovation in their organizations. Career changers looking to transition into the booming AI industry in India, leveraging their analytical and technical aptitude, will find this program highly beneficial.
Why Choose This Course?
Graduates of this program can expect promising career paths as AI Engineers, Machine Learning Scientists, Data Scientists, and Research Analysts in top Indian and multinational companies. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals commanding significantly higher packages. The program fosters critical thinking and problem-solving, aligning graduates with opportunities for growth and leadership in India''''s technology sector, potentially leading to specialized certifications.
Student Success Practices
Foundation Stage
Strengthen Mathematical & Algorithmic Foundations- (Semester 1)
Dedicate time to master advanced engineering mathematics, data structures, and algorithms. These subjects form the bedrock of AI and ML. Regularly solve problems and implement algorithms to solidify understanding.
Tools & Resources
NPTEL courses, LeetCode/HackerRank for coding practice, Khan Academy for math refreshers
Career Connection
A strong foundation is crucial for understanding complex AI models, designing efficient algorithms, and excelling in technical interviews for core AI roles.
Develop Core Programming Proficiency in AI- (Semester 1)
Become highly proficient in Python programming, along with key libraries like NumPy, Pandas, Scikit-learn, and Matplotlib. Actively participate in lab sessions and build small projects.
Tools & Resources
Python Documentation, Kaggle tutorials, GitHub for version control
Career Connection
Python is the primary language for AI/ML development. Proficiency ensures you can implement, experiment with, and deploy AI solutions efficiently in industry.
Grasp Foundational Machine Learning Concepts- (Semester 1)
Focus on thoroughly understanding the theoretical underpinnings of Machine Learning. Actively engage with course material, attend workshops, and start reading research papers relevant to basic ML algorithms.
Tools & Resources
DeepLearning.AI courses, Andrew Ng''''s Machine Learning course, Standard ML textbooks
Career Connection
A clear understanding of ML fundamentals is essential before diving into specialized areas like Deep Learning or NLP, providing a conceptual edge.
Intermediate Stage
Engage in Applied AI Projects & Specialization- (Semester 2)
Actively apply learned Deep Learning, NLP, and Computer Vision techniques to build practical projects. Use electives to delve into areas like Information Retrieval or Multi-agent Systems that align with your career interests.
Tools & Resources
TensorFlow/PyTorch, OpenCV, Hugging Face library, Kaggle competitions
Career Connection
Practical projects demonstrate your ability to convert theoretical knowledge into real-world applications, a key requirement for AI/ML engineering roles.
Network and Seek Industry Exposure- (Semester 2)
Attend industry seminars, guest lectures, and workshops organized by the department or local AI communities. Start connecting with professionals on platforms like LinkedIn to understand industry trends and potential internship opportunities.
Tools & Resources
LinkedIn, AI/ML meetups in Bhubaneswar/nearby cities, KIIT''''s industry interaction events
Career Connection
Networking opens doors to internships, mentorship, and helps in understanding industry expectations, making you more market-ready.
Cultivate Research Skills and Critical Thinking- (Semester 2)
Begin exploring research topics through literature reviews, focusing on current challenges and innovations in AI. Actively participate in group discussions and present findings to develop analytical and communication skills.
Tools & Resources
IEEE Xplore, arXiv, Google Scholar, ResearchGate
Career Connection
Strong research skills are vital for the M.Tech thesis and for roles in R&D, preparing you for advanced problem-solving in your career.
Advanced Stage
Execute a Robust M.Tech Thesis Project- (Semesters 3-4)
Dedicate significant effort to your M.Tech Thesis (Part I & II). Choose a challenging problem, develop an innovative solution, rigorously test it, and document your findings meticulously. Aim for a high-quality publication.
Tools & Resources
LaTeX for thesis writing, Academic advisors, Access to GPU resources if needed
Career Connection
A strong thesis showcases your capability for independent research and problem-solving, making you a strong candidate for R&D roles or higher studies.
Prepare Comprehensively for Placements- (Semesters 3-4)
Actively participate in placement preparatory activities, including mock interviews, resume building workshops, and aptitude tests. Focus on company-specific preparation and revise core AI/ML concepts thoroughly.
Tools & Resources
KIIT Placement Cell resources, Online interview platforms, Company-specific previous year questions
Career Connection
Effective placement preparation is critical for securing desirable job offers from leading companies in the competitive Indian job market.
Develop Professional Communication & Presentation Skills- (Semesters 3-4)
Regularly practice presenting your project work and research findings in a clear, concise, and engaging manner. This includes technical writing for reports and verbal presentations for seminars and thesis defense.
Tools & Resources
Toastmasters International (if available), Presentation software (PowerPoint, Google Slides), Feedback from peers and faculty
Career Connection
Strong communication skills are essential for collaborating in teams, presenting ideas to stakeholders, and advancing into leadership roles in any tech organization.
Program Structure and Curriculum
Eligibility:
- B.Tech/B.E./MCA/M.Sc. in Computer Science & Engineering/Information Technology/Electronics/Electrical/Instrumentation/Telecommunications/Data Science/Computer Engineering/Software Engineering or equivalent with 60% aggregate marks or equivalent CGPA. Valid GATE score or KIITEE M.Tech score preferred.
Duration: 4 semesters / 2 years
Credits: 72 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PMA-1101 | Advanced Engineering Mathematics | Core | 3 | Linear Algebra, Probability and Statistics, Optimization Techniques, Stochastic Processes, Graph Theory |
| PCS-1101 | Advanced Data Structures & Algorithms | Core | 3 | Analysis of Algorithms, Advanced Data Structures, Hashing Techniques, Graph Algorithms, Dynamic Programming, String Matching Algorithms |
| PCS-1102 | Advanced Computer Architecture | Core | 3 | CPU Design Principles, Memory Hierarchy and Caching, Pipelining and Instruction Level Parallelism, Vector and Array Processors, Multi-core Architectures, Interconnection Networks |
| PCS-1103 | Advanced Database Management Systems | Core | 3 | Relational Model and Query Processing, Transaction Management, Concurrency Control, Distributed Databases, Data Warehousing, NoSQL Databases |
| PEC-1101 | Machine Learning | Core | 3 | Supervised Learning Algorithms, Unsupervised Learning Techniques, Reinforcement Learning Basics, Neural Networks Fundamentals, Ensemble Methods, Model Evaluation and Selection |
| OEC-1101 | Research Methodology | Core | 3 | Research Design and Problem Formulation, Literature Review and Referencing, Data Collection Methods, Statistical Analysis for Research, Report Writing and Presentation, Ethical Issues in Research |
| PCL-1101 | Advanced Data Structures & Algorithms Lab | Lab | 2 | Implementation of Advanced Data Structures, Algorithm Design and Analysis, Problem Solving using Data Structures, Performance Evaluation of Algorithms |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PEC-1201 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers and Attention Mechanisms, Generative Models (GANs, VAEs), Deep Reinforcement Learning |
| PEC-1202 | Natural Language Processing | Core | 3 | Text Preprocessing and Tokenization, Language Models and N-grams, Word Embeddings (Word2Vec, GloVe), Part-of-Speech Tagging and Parsing, Named Entity Recognition, Machine Translation |
| PEC-1203 | Computer Vision | Core | 3 | Image Filtering and Enhancement, Feature Detection and Description, Image Segmentation and Grouping, Object Recognition and Detection, Image Classification with Deep Learning, Motion Estimation and Tracking |
| PEC-1204 | AI Elective-I (Information Retrieval) | Elective | 3 | Information Retrieval Models, Indexing and Query Processing, Web Search and Link Analysis, Evaluation Metrics for IR, Recommender Systems, Text Mining for IR |
| PEC-1205 | AI Elective-II (Multi-agent Systems) | Elective | 3 | Agent Architectures, Game Theory and Strategic Interaction, Cooperation and Coordination, Distributed Artificial Intelligence, Negotiation and Bargaining, Learning in Multi-agent Systems |
| OEC-1201 | Intellectual Property Rights and Cyber Law | Core | 3 | Patents, Copyrights, and Trademarks, Trade Secrets and Industrial Designs, Cybercrime and IT Act, Data Protection and Privacy Laws, Digital Signatures and Electronic Contracts, Legal Aspects of E-commerce |
| PCL-1201 | Artificial Intelligence Lab | Lab | 2 | Implementation of Machine Learning Algorithms, Deep Learning Frameworks (TensorFlow, PyTorch), Natural Language Processing Applications, Computer Vision Project Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCS-2101 | Research Seminar | Core | 2 | Research Paper Analysis, Scientific Writing, Technical Presentation Skills, Literature Review Techniques |
| PEC-2101 | AI Elective-III (Cognitive Computing) | Elective | 3 | Cognitive Architectures, Natural Language Understanding, Knowledge Representation and Reasoning, Perception and Action in AI, Human-Computer Interaction in Cognitive Systems, Learning and Memory in AI |
| PEC-2102 | AI Elective-IV (Advanced Deep Learning) | Elective | 3 | Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Graph Neural Networks (GNNs), Explainable AI (XAI), Federated Learning, Transformers and Advanced Attention |
| PCD-2101 | M.Tech. Thesis Part-I | Project | 8 | Problem Identification and Formulation, Extensive Literature Survey, Methodology Design and Planning, Preliminary Implementation and Results, Thesis Proposal Writing, Research Ethics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCD-2201 | M.Tech. Thesis Part-II | Project | 16 | System Implementation and Development, Extensive Experimentation and Data Analysis, Results Interpretation and Discussion, Comprehensive Thesis Writing, Thesis Defense and Presentation, Publication of Research Findings |
