.png&w=1920&q=75)
B-TECH in Artificial Intelligence Machine Learning at Guru Nanak Khalsa Institute of Technology and Management
Yamunanagar, Haryana
.png&w=1920&q=75)
About the Specialization
What is Artificial Intelligence & Machine Learning at Guru Nanak Khalsa Institute of Technology and Management Yamunanagar?
This Artificial Intelligence & Machine Learning program at Guru Nanak Khalsa Institute of Technology and Management focuses on equipping students with advanced theoretical knowledge and practical skills in AI and ML domains. The curriculum is designed to meet the burgeoning demand for skilled professionals in areas like intelligent systems, data analysis, and predictive modeling within the Indian technology landscape, preparing graduates for cutting-edge roles.
Who Should Apply?
This program is ideal for ambitious fresh graduates holding a 10+2 qualification with Physics, Mathematics, and one additional science/technical subject, aspiring to build a career in the rapidly evolving AI and ML sector. It also caters to individuals looking to upskill or career changers from related technical fields, providing a strong foundation for innovative problem-solving and system development.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including AI Engineer, Machine Learning Scientist, Data Scientist, and NLP Specialist. Entry-level salaries typically range from INR 4-8 LPA, with significant growth potential as experience increases. The program fosters critical thinking and analytical abilities, aligning with industry demand for expertise in developing intelligent solutions across various Indian sectors.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Develop a strong base in C, C++, and Python programming. Focus on understanding data structures and algorithms through rigorous practice. Participate in coding challenges regularly to improve problem-solving speed and logic.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation
Career Connection
Essential for clearing technical rounds in placements, building foundational skills for AI/ML development roles.
Build Strong Mathematical Acumen- (Semester 1-2)
Prioritize understanding Engineering Mathematics, Discrete Mathematics, and Probability & Statistics. These form the bedrock for advanced AI/ML concepts. Seek out extra tutorials or online courses for conceptual clarity.
Tools & Resources
NPTEL courses, Khan Academy, specific textbooks, peer study groups
Career Connection
Crucial for comprehending complex algorithms, research roles, and advanced specialization in AI/ML.
Cultivate Effective Study Habits & Peer Learning- (Semester 1-2)
Form study groups to discuss complex topics, prepare for exams, and review lab experiments. Actively participate in class, ask questions, and utilize faculty office hours. Focus on time management and consistent daily study.
Tools & Resources
Google Meet/Zoom for group studies, university library, academic advisors
Career Connection
Enhances collaborative skills, critical for team projects in the industry, and fosters a deeper understanding of subjects.
Intermediate Stage
Engage in Project-Based Learning- (Semester 3-5)
Apply theoretical knowledge from AI&ML, DBMS, and OS courses by undertaking mini-projects. Focus on implementing algorithms, building small applications, and solving real-world problems. Utilize open-source datasets and frameworks.
Tools & Resources
GitHub, Kaggle, TensorFlow/PyTorch tutorials, Jupyter Notebooks
Career Connection
Creates a portfolio of practical work, highly valued by employers for demonstrating application skills during internships and placements.
Seek Early Industry Exposure- (Semester 4-5)
Actively look for summer internships after the 4th semester or during semester breaks. Attend workshops, webinars, and guest lectures by industry experts. Network with professionals on platforms like LinkedIn.
Tools & Resources
LinkedIn, Internshala, college placement cell, industry specific hackathons
Career Connection
Provides invaluable real-world experience, helps identify career interests, and often leads to pre-placement offers.
Develop Specialized AI/ML Skills- (Semester 4-5)
Beyond core curriculum, delve into specific areas like Deep Learning, NLP, or Computer Vision. Take online certifications or specialized courses to gain expertise in these trending domains. Participate in relevant competitions.
Tools & Resources
Coursera, edX, Udemy, DataCamp, Analytics Vidhya
Career Connection
Differentiates candidates in a competitive job market, enabling entry into niche and high-paying roles in AI/ML.
Advanced Stage
Focus on Capstone Projects & Research- (Semester 6-8)
Dedicate significant effort to the Major Projects (Minor Project-I, Major Project-II/Internship-II, Major Project-III/Dissertation). Choose topics that are challenging, innovative, and ideally solve a real-world problem or contribute to research.
Tools & Resources
Research papers (ArXiv, IEEE Xplore), open-source AI libraries, faculty mentors, industry collaborations
Career Connection
Showcases advanced problem-solving, research capabilities, and technical depth, critical for R&D roles, higher studies, and leading industry positions.
Intensive Placement Preparation- (Semester 7-8)
Start comprehensive preparation for placements well in advance. Practice aptitude, logical reasoning, verbal ability, and technical interview questions (DSA, OS, DBMS, Networks, AI/ML concepts). Conduct mock interviews.
Tools & Resources
Career services, placement cell resources, InterviewBit, GeeksforGeeks, Glassdoor
Career Connection
Maximizes chances of securing desirable placements in top-tier Indian and multinational companies.
Build a Professional Network & Portfolio- (Semester 6-8)
Continuously update your LinkedIn profile, showcasing projects, internships, and skills. Attend industry conferences (virtual or physical), connect with alumni, and seek mentorship. Maintain a well-structured online portfolio of your work.
Tools & Resources
LinkedIn, GitHub portfolio, professional networking events, alumni association
Career Connection
Opens doors to hidden job opportunities, provides career guidance, and enhances visibility within the AI/ML community in India.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics and Mathematics along with one of Chemistry/Biotechnology/Biology/Technical Vocational subject with minimum 45% (40% for SC/ST)
Duration: 4 years / 8 semesters
Credits: 163 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS-101 | Engineering Mathematics-I | Core | 3 | Matrices, Calculus of Single Variable, Multivariable Calculus, Sequence and Series, Complex Numbers |
| BS-103 | Engineering Physics | Core | 3 | Wave Optics, Lasers and Fibre Optics, Quantum Mechanics, Solid State Physics, Semiconductor Physics |
| HS-101 | English for Professionals | Core | 2 | Communication Skills, Grammar and Vocabulary, Reading Comprehension, Public Speaking, Report Writing |
| ES-101 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits (Single & Three Phase), Transformers, DC Machines, Induction Motors |
| ES-103 | Engineering Graphics & Design | Core | 1 | Drawing Instruments, Orthographic Projection, Isometric Projection, Sections of Solids, AutoCAD Basics |
| ES-105 | Programming for Problem Solving | Core | 3 | C Programming Fundamentals, Data Types and Operators, Control Structures, Functions and Pointers, Arrays and Structures, File I/O |
| BS-101(L) | Engineering Mathematics-I Lab | Lab | 0 | |
| BS-103(L) | Engineering Physics Lab | Lab | 1 | |
| HS-101(L) | English for Professionals Lab | Lab | 1 | |
| ES-101(L) | Basic Electrical Engineering Lab | Lab | 1 | |
| ES-105(L) | Programming for Problem Solving Lab | Lab | 1 | |
| ES-107(L) | Manufacturing Practices (Workshop) | Lab | 1 |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS-102 | Engineering Mathematics-II | Core | 3 | Linear Algebra, Ordinary Differential Equations, Laplace Transforms, Fourier Series, Probability & Statistics |
| BS-104 | Engineering Chemistry | Core | 3 | Water Technology, Fuels and Combustion, Polymers, Corrosion and its Control, Phase Rule |
| HS-102 | Environmental Science | Core | 0 | Natural Resources, Ecosystems, Environmental Pollution, Social Issues and the Environment, Human Population and Environment |
| ES-102 | Electronic Devices & Circuits | Core | 3 | Semiconductor Diodes, Bipolar Junction Transistors, Field Effect Transistors, Rectifiers and Filters, Amplifiers and Oscillators |
| ES-104 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Algorithms, Sorting and Searching Techniques |
| ES-106 | Object-Oriented Programming using C++ | Core | 3 | OOP Concepts (Classes, Objects), Inheritance and Polymorphism, Constructors and Destructors, Operator Overloading, Exception Handling and Templates |
| BS-102(L) | Engineering Mathematics-II Lab | Lab | 0 | |
| BS-104(L) | Engineering Chemistry Lab | Lab | 1 | |
| ES-102(L) | Electronic Devices & Circuits Lab | Lab | 1 | |
| ES-104(L) | Data Structures Lab | Lab | 1 | |
| ES-106(L) | Object-Oriented Programming using C++ Lab | Lab | 1 | |
| ES-108(L) | Python Programming Lab | Lab | 1 |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ES-201 | Digital Electronics | Core | 3 | Logic Gates and Boolean Algebra, Combinational Circuits, Sequential Circuits, Registers and Counters, Memory Devices |
| CS-201 | Discrete Mathematics | Core | 3 | Set Theory and Relations, Functions and Permutations, Propositional and Predicate Logic, Graph Theory, Recurrence Relations and Generating Functions |
| AI-201 | Introduction to AI & ML | Core | 3 | History and Foundations of AI, Problem Solving Agents (Search), Knowledge Representation, Introduction to Machine Learning, Supervised and Unsupervised Learning, Evaluation Metrics |
| CS-203 | Database Management Systems | Core | 3 | DBMS Architecture, Entity-Relationship Model, Relational Algebra and Calculus, Structured Query Language (SQL), Normalization and Transaction Management |
| CS-205 | Computer Organization & Architecture | Core | 3 | Basic Computer System Organization, CPU Design and Instruction Sets, Memory Hierarchy, Input/Output Organization, Pipelining and Parallel Processing |
| ES-201(L) | Digital Electronics Lab | Lab | 1 | |
| AI-201(L) | Introduction to AI & ML Lab | Lab | 1 | |
| CS-203(L) | Database Management Systems Lab | Lab | 1 | |
| CS-207(L) | UNIX/Linux Programming Lab | Lab | 1 | |
| HS-201 | Universal Human Values | Core | 3 | Self Exploration and Happiness, Harmony in Family and Society, Harmony in Nature/Existence, Ethical Human Conduct, Professional Ethics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS-202 | Professional Ethics | Core | 2 | Engineering Ethics, Moral Autonomy and Theories, Code of Ethics, Safety, Responsibilities and Rights, Global Issues |
| CS-202 | Operating Systems | Core | 3 | Operating System Structure, Process Management and CPU Scheduling, Memory Management, File Systems, I/O Systems and Deadlocks |
| CS-204 | Design & Analysis of Algorithms | Core | 3 | Algorithm Analysis and Complexity, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms, NP-Completeness |
| AI-202 | Machine Learning Techniques | Core | 3 | Regression Models, Classification Algorithms (SVM, Decision Trees), Clustering Techniques (K-Means, Hierarchical), Dimensionality Reduction (PCA), Ensemble Methods (Bagging, Boosting) |
| AI-204 | Probability and Statistics for AI | Core | 3 | Probability Theory, Random Variables and Distributions, Hypothesis Testing, Regression and Correlation, Bayesian Inference |
| AI-206 | Data Mining & Warehousing | Core | 3 | Data Warehouse Architecture, ETL Process and OLAP, Data Preprocessing, Association Rule Mining, Classification and Clustering |
| CS-202(L) | Operating Systems Lab | Lab | 1 | |
| AI-202(L) | Machine Learning Techniques Lab | Lab | 1 | |
| AI-206(L) | Data Mining & Warehousing Lab | Lab | 1 | |
| ES-202(L) | Web Technology Lab | Lab | 1 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS-301 | Computer Networks | Core | 3 | Network Topologies and Models (OSI, TCP/IP), Data Link Layer, Network Layer (Routing), Transport Layer (TCP, UDP), Application Layer Protocols |
| AI-301 | Deep Learning | Core | 3 | Artificial Neural Networks, Backpropagation and Optimization, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs, LSTMs), Deep Learning Frameworks (TensorFlow/PyTorch) |
| AI-303 | Natural Language Processing | Core | 3 | NLP Tasks and Applications, Text Preprocessing and Tokenization, Part-of-Speech Tagging, Named Entity Recognition, Word Embeddings and Transformers |
| PE-AIML-X | Program Elective-I | Elective | 3 | Reinforcement Learning (PE-AIML-1), Computer Vision (PE-AIML-2), Information Retrieval (PE-AIML-3) |
| OE-X | Open Elective-I | Elective | 3 | Data Structures (OE-101), Introduction to Web Technology (OE-102), Digital Marketing (OE-103), Cyber Security & Cyber Laws (OE-104) |
| CS-301(L) | Computer Networks Lab | Lab | 1 | |
| AI-301(L) | Deep Learning Lab | Lab | 1 | |
| AI-303(L) | Natural Language Processing Lab | Lab | 1 | |
| AI-305 | Minor Project-I | Project | 2 | Project Planning and Design, Literature Review, Implementation and Testing, Report Writing, Presentation Skills |
| AI-307 | Industrial Training | Core | 3 | Industry Exposure, Practical Skill Application, Report Documentation, Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS-301 | Entrepreneurship | Core | 2 | Entrepreneurial Mindset, Business Idea Generation, Business Plan Development, Marketing and Finance for Startups, Legal Aspects and Funding |
| AI-302 | AI Ethics & Governance | Core | 3 | Ethical AI Principles, Bias, Fairness and Accountability, Transparency and Explainability, AI Regulations and Policies, Data Privacy and Security in AI |
| AI-304 | Big Data Analytics | Core | 3 | Big Data Ecosystem (Hadoop, Spark), Distributed File Systems (HDFS), MapReduce Programming Model, NoSQL Databases, Data Streaming and Cloud Big Data |
| PE-AIML-X | Program Elective-II | Elective | 3 | Advanced Deep Learning (PE-AIML-4), Ethical Hacking (PE-AIML-5), Robotics Process Automation (PE-AIML-6) |
| OE-X | Open Elective-II | Elective | 3 | Software Engineering (OE-201), Internet of Things (OE-202), Cloud Computing (OE-203), Agile Methodology (OE-204) |
| AI-302(L) | AI Ethics & Governance Lab | Lab | 1 | |
| AI-304(L) | Big Data Analytics Lab | Lab | 1 | |
| AI-306(L) | Advanced AI Lab (based on PE-II) | Lab | 1 | |
| AI-308 | Summer Industrial Training/Project | Project/Training | 2 | Industry Specific Project Work, Practical Skill Enhancement, Problem Solving, Report Submission |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI-401 | Explainable AI | Core | 3 | Interpretability vs. Explainability, Feature Importance (LIME, SHAP), Model-agnostic Explanations, Causal Inference in AI, Trustworthy AI Systems |
| PE-AIML-X | Program Elective-III | Elective | 3 | Data Visualization (PE-AIML-7), Cognitive Computing (PE-AIML-8), Computer Vision & Image Processing (PE-AIML-9) |
| PE-AIML-X | Program Elective-IV | Elective | 3 | IoT for AI (PE-AIML-10), Human Computer Interaction (PE-AIML-11), Distributed AI (PE-AIML-12) |
| OE-X | Open Elective-III | Elective | 3 | Principles of Management (OE-301), Introduction to Data Science (OE-302), Soft Computing (OE-303), Blockchain Technology (OE-304) |
| AI-403(L) | Explainable AI Lab | Lab | 1 | |
| AI-405 | Major Project-II / Internship-II | Project/Internship | 8 | Advanced Project Development, Research and Innovation, Real-world Problem Solving, Comprehensive Report and Presentation |
| AI-407 | Seminar | Seminar | 1 | Technical Presentation Skills, Research Topic Selection, Literature Review, Public Speaking |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PE-AIML-X | Program Elective-V | Elective | 3 | Conversational AI (PE-AIML-13), AI for Healthcare (PE-AIML-14), Game Theory & AI (PE-AIML-15) |
| OE-X | Open Elective-IV | Elective | 3 | Intellectual Property Rights (OE-401), Human Resource Management (OE-402), Financial Management (OE-403), Cryptography & Network Security (OE-404) |
| AI-402 | Major Project-III / Dissertation | Project/Dissertation | 10 | In-depth Research and Analysis, Innovative Solution Design, System Implementation and Evaluation, Thesis Writing and Defense, Contribution to Knowledge |
