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B-TECH in Artificial Intelligence Machine Learning at RP Indraprastha Institute of Technology & Management
Karnal, Haryana
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About the Specialization
What is Artificial Intelligence & Machine Learning at RP Indraprastha Institute of Technology & Management Karnal?
This Artificial Intelligence & Machine Learning program at RP Indraprastha Institute of Technology & Management focuses on equipping students with expertise in intelligent systems design and data-driven decision-making. It delves into the core principles and advanced applications of AI and ML, catering to the burgeoning demand for these skills across various Indian industries. The program emphasizes a blend of theoretical knowledge and practical implementation, fostering innovation.
Who Should Apply?
This program is ideal for fresh graduates with a strong foundation in science and mathematics, aspiring to build careers in cutting-edge technology. It also suits working professionals looking to pivot or upskill in AI/ML, and career changers from related engineering fields. A keen interest in logical problem-solving, data analysis, and developing smart solutions is a key prerequisite for success.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including AI Engineer, Machine Learning Specialist, Data Scientist, and AI Consultant in sectors like IT, finance, healthcare, and e-commerce. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The program prepares students for industry certifications and higher studies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice coding problems on platforms like HackerRank and CodeChef to solidify logic, problem-solving abilities, and implementation skills essential for advanced AI/ML algorithms. Focus on efficient data structure usage.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, NPTEL courses on Data Structures
Career Connection
Strong coding skills are non-negotiable for AI/ML roles, serving as the bedrock for understanding and implementing complex models.
Develop Mathematical Acumen- (Semester 1-2)
Actively engage with advanced mathematics courses, solving numerous problems to deeply understand the theoretical underpinnings of AI/ML algorithms. Seek supplementary resources and tutorials to build a robust foundation.
Tools & Resources
Khan Academy, NPTEL videos, Textbooks like ''''Introduction to Linear Algebra'''' by Gilbert Strang
Career Connection
A robust mathematical foundation is crucial for grasping algorithm mechanics, debugging models, and conducting research in AI/ML.
Initiate Peer Learning Groups- (Semester 1-2)
Form study groups to discuss complex topics, share insights, and work together on assignments and mini-projects. Explaining concepts to others reinforces understanding and builds teamwork skills.
Tools & Resources
Google Meet, Discord, Shared online whiteboards, College library group study rooms
Career Connection
Teamwork and communication skills gained are highly valued in industry, where AI/ML projects are often collaborative.
Intermediate Stage
Engage in Mini-Projects and Kaggle Competitions- (Semester 3-5)
Actively participate in mini-projects, hackathons, and Kaggle competitions. This practical application solidifies understanding of machine learning algorithms, data preprocessing, and model evaluation techniques.
Tools & Resources
Kaggle, GitHub, scikit-learn, TensorFlow, PyTorch
Career Connection
A strong portfolio of projects and competition achievements demonstrates practical skills to potential employers, enhancing placement prospects.
Seek Early Industry Exposure- (Semester 3-5)
Look for summer internships, even short-term ones, in startups or smaller companies working on AI/ML. Additionally, complete online certifications from platforms like Coursera or edX in specific AI/ML tools or domains.
Tools & Resources
LinkedIn, Internshala, Coursera, edX, Udemy
Career Connection
Early exposure provides practical industry context, helps in networking, and makes the resume stand out for full-time roles.
Specialize in Key ML Areas- (Semester 4-5)
Beyond core courses, choose electives and self-study to specialize in areas like Deep Learning, Computer Vision, or Natural Language Processing based on interest. Master relevant libraries and frameworks.
Tools & Resources
OpenCV, NLTK, spaCy, Hugging Face, Specific research papers
Career Connection
Specialization makes you a more attractive candidate for specific roles and provides a competitive edge in a crowded job market.
Advanced Stage
Undertake Capstone Project with Impact- (Semester 7-8)
Dedicate significant effort to a capstone project that solves a real-world problem, potentially collaborating with industry. Aim for innovation, robust implementation, and clear documentation.
Tools & Resources
Latest AI/ML frameworks, Cloud platforms (AWS, Azure, GCP), Project management tools
Career Connection
A strong capstone project is often a key talking point in interviews and can directly lead to placement offers or entrepreneurial ventures.
Intensive Placement Preparation- (Semester 7-8)
Engage in rigorous preparation for campus placements, including technical interviews focusing on AI/ML concepts, data structures, algorithms, and behavioral questions. Participate in mock interviews with faculty and seniors.
Tools & Resources
InterviewBit, LeetCode, Company-specific interview guides, College placement cell resources
Career Connection
Thorough preparation directly impacts success in securing desirable job offers from top companies during campus placements.
Build a Professional Network and Personal Brand- (Semester 6-8)
Attend AI/ML conferences, workshops, and meetups (online/offline) to network with professionals and researchers. Maintain an active LinkedIn profile and contribute to open-source AI/ML projects.
Tools & Resources
LinkedIn, GitHub, Industry conferences (e.g., Data Science Congress, India AI Summit)
Career Connection
A strong professional network can open doors to opportunities beyond campus placements, including referrals, mentorship, and entrepreneurial collaborations.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics and Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Technical Vocational subject. Obtained at least 45% marks (40% in case of reserved category) in the above subjects taken together.
Duration: 8 semesters / 4 years
Credits: 165 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HSMC-HS101G | English | Humanities & Social Sciences including Management Course | 2 | Communication Skills, Grammar and Usage, Reading Comprehension, Report Writing, Presentation Skills |
| BSC-MA101G | Mathematics-I | Basic Science Course | 4 | Calculus, Matrices, Differential Equations, Vector Calculus, Sequences and Series |
| BSC-PH101G | Applied Physics-I | Basic Science Course | 3 | Optics, Quantum Mechanics, Solid State Physics, Lasers, Fiber Optics |
| ESC-ES101G | Basic Electrical Engineering | Engineering Science Course | 3 | DC/AC Circuits, Network Theorems, Transformers, Motors, Power Systems |
| ESC-ES103G | Programming for Problem Solving | Engineering Science Course | 3 | C Programming Fundamentals, Data Types and Operators, Control Flow Statements, Functions and Arrays, Pointers and Structures |
| HSMC-HS103G | Environmental Studies | Humanities & Social Sciences including Management Course | 0 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Waste Management, Environmental Ethics |
| BSC-PH103G | Applied Physics-I Lab | Basic Science Course | 1 | Optical Instruments, Semiconductor Devices, Electromagnetic Experiments, Wave Phenomena, Error Analysis |
| ESC-ES105G | Basic Electrical Engineering Lab | Engineering Science Course | 1 | Circuit Laws Verification, AC/DC Motor Characteristics, Transformer Testing, Power Measurement, Wiring Practices |
| ESC-ES107G | Programming for Problem Solving Lab | Engineering Science Course | 1 | C Program Implementation, Debugging Techniques, Algorithm Tracing, Input/Output Operations, Conditional Logic |
| HSMC-HS105G | English Lab | Humanities & Social Sciences including Management Course | 1 | Pronunciation Practice, Group Discussions, Interview Skills, Listening Comprehension, Public Speaking |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC-MA201G | Mathematics-II | Basic Science Course | 4 | Complex Analysis, Probability Theory, Statistics, Laplace Transforms, Fourier Series |
| BSC-CH201G | Applied Chemistry | Basic Science Course | 3 | Water Treatment, Fuels and Combustion, Polymers and Composites, Corrosion and its Control, Electrochemistry |
| ESC-ES201G | Engineering Graphics & Design | Engineering Science Course | 2 | Engineering Drawing Standards, Orthographic Projections, Sectional Views, Isometric Projections, Introduction to CAD |
| ESC-ES203G | Data Structures & Algorithms | Engineering Science Course | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| ESC-ES205G | Basic Electronics Engineering | Engineering Science Course | 3 | Semiconductor Diodes, Bipolar Junction Transistors, Operational Amplifiers, Digital Logic Gates, Rectifiers and Filters |
| ESC-ES207G | Workshop Manufacturing Practices | Engineering Science Course | 1 | Fitting and Carpentry, Welding Techniques, Sheet Metal Operations, Foundry Practices, Machining Processes |
| BSC-CH203G | Applied Chemistry Lab | Basic Science Course | 1 | Water Quality Testing, Chemical Synthesis, Spectrophotometry, Titration Techniques, Corrosion Analysis |
| ESC-ES209G | Data Structures & Algorithms Lab | Engineering Science Course | 1 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| ESC-ES211G | Basic Electronics Engineering Lab | Engineering Science Course | 1 | Diode Characteristics, Transistor Amplifiers, Logic Gate Verification, Op-Amp Applications, Rectifier Circuit Design |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC-CS201G | Discrete Mathematics | Program Core Course | 3 | Set Theory, Logic and Proof Techniques, Relations and Functions, Graph Theory, Combinatorics |
| PCC-CS203G | Digital Electronics | Program Core Course | 3 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Flip-Flops and Counters |
| PCC-CS205G | Object-Oriented Programming | Program Core Course | 3 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Abstract Classes and Interfaces, Exception Handling |
| PCC-AIML201G | Introduction to AI & ML | Program Core Course | 3 | History of AI, Problem-Solving Agents, Search Algorithms, Machine Learning Basics, Supervised and Unsupervised Learning |
| PCC-AIML203G | Data Communication & Computer Networks | Program Core Course | 3 | OSI Model, TCP/IP Protocol Suite, Network Topologies, Data Transmission Media, Routing and Switching |
| BSC-MA205G | Probability & Statistics | Basic Science Course | 3 | Random Variables, Probability Distributions, Hypothesis Testing, Regression Analysis, Statistical Inference |
| PCC-CS207G | Digital Electronics Lab | Program Core Course | 1 | Logic Gate Implementation, Combinational Circuit Design, Sequential Circuit Realization, Multiplexers and Demultiplexers, Flip-Flop Applications |
| PCC-CS209G | Object-Oriented Programming Lab | Program Core Course | 1 | Class and Object Implementation, Inheritance and Polymorphism Practice, File Handling, GUI Programming Basics, Data Encapsulation |
| PCC-AIML205G | Introduction to AI & ML Lab | Program Core Course | 1 | Search Algorithms Implementation, Data Preprocessing, Basic Classification Models, Regression Algorithms, Evaluation Metrics |
| PCC-AIML207G | Data Communication & Computer Networks Lab | Program Core Course | 1 | Network Configuration, Socket Programming, Packet Tracing, Network Protocols Implementation, Client-Server Communication |
| HSMC-HS201G | Universal Human Values | Humanities & Social Sciences including Management Course | 0 | Self-Exploration, Harmony in Society, Professional Ethics, Holistic Living, Human Values and Principles |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC-CS202G | Operating Systems | Program Core Course | 3 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks and Synchronization |
| PCC-CS204G | Design & Analysis of Algorithms | Program Core Course | 3 | Asymptotic Notations, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| PCC-CS206G | Database Management Systems | Program Core Course | 3 | ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| PCC-AIML202G | Machine Learning | Program Core Course | 3 | Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees, Clustering Algorithms (K-Means) |
| PCC-AIML204G | Computer Organization & Architecture | Program Core Course | 3 | CPU Structure and Function, Memory Hierarchy, I/O Organization, Pipelining, Instruction Set Architectures |
| ESC-CS202G | Software Engineering | Engineering Science Course | 3 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing, Project Management |
| PCC-CS208G | Operating Systems Lab | Program Core Course | 1 | Shell Programming, Process Management Commands, Thread Synchronization, Memory Allocation Techniques, System Calls |
| PCC-CS210G | Database Management Systems Lab | Program Core Course | 1 | SQL DDL and DML Commands, Schema Design, Stored Procedures, Trigger Implementation, Database Connectivity |
| PCC-AIML206G | Machine Learning Lab | Program Core Course | 1 | Linear Model Implementation, SVM and Decision Tree Practice, Clustering Algorithms, Model Evaluation and Validation, Feature Engineering |
| ESC-CS204G | Software Engineering Lab | Engineering Science Course | 1 | Requirements Gathering, UML Diagramming, Software Design Patterns, Test Case Generation, Version Control Systems |
| PCC-AIML208G | Mini Project-I | Program Core Course | 1 | Problem Identification, Project Planning, Basic System Development, Testing and Debugging, Documentation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC-CS301G | Theory of Automata and Computation | Program Core Course | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Turing Machines, Undecidability |
| PCC-AIML301G | Artificial Intelligence | Program Core Course | 3 | Knowledge Representation, Expert Systems, Fuzzy Logic, Genetic Algorithms, Game Theory |
| PCC-AIML303G | Deep Learning | Program Core Course | 3 | Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Transfer Learning, Optimization Techniques |
| PCC-CS303G | Compiler Design | Program Core Course | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| PCC-AIML305G | Computer Graphics | Program Core Course | 3 | Graphics Primitives, 2D/3D Transformations, Viewing and Clipping, Projections, Shading and Illumination Models |
| OEC-CS301G | Open Elective-I (Example: Cyber Security) | Open Elective Course | 3 | Network Security, Cryptography Basics, Cyber Attacks, Security Policies, Digital Forensics |
| PCC-AIML307G | Artificial Intelligence Lab | Program Core Course | 1 | Logic Programming (Prolog), Expert System Development, Fuzzy Logic Implementation, Genetic Algorithm Application, Search Agent Design |
| PCC-AIML309G | Deep Learning Lab | Program Core Course | 1 | Neural Network Implementation, CNN for Image Classification, RNN for Sequence Data, TensorFlow/PyTorch Practice, Hyperparameter Tuning |
| PCC-AIML311G | Mini Project-II | Program Core Course | 1 | Advanced Problem Definition, System Architecture Design, Module Integration, Performance Optimization, Technical Presentation |
| PCC-CS305G | Compiler Design Lab | Program Core Course | 1 | Lexical Analyzer Implementation, Parsing Techniques, Syntax Directed Translation, Code Generation, Symbol Table Management |
| PCC-AIML313G | Computer Graphics Lab | Program Core Course | 1 | Drawing Algorithms, Geometric Transformations, Clipping Algorithms, 3D Rendering Basics, Interactive Graphics Programming |
| GMC-MC901G | Essence of Indian Traditional Knowledge | General Minor Course | 0 | Indian Philosophical Systems, Traditional Arts and Literature, Yoga and Ayurveda, Indian Architecture, Sustainable Practices |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC-AIML302G | Data Science | Program Core Course | 3 | Data Wrangling, Exploratory Data Analysis, Feature Engineering, Data Visualization, Predictive Modeling |
| PCC-AIML304G | Natural Language Processing | Program Core Course | 3 | Text Preprocessing, N-grams and Word Embeddings, Part-of-Speech Tagging, Sentiment Analysis, Text Generation Models |
| PCC-AIML306G | Internet of Things | Program Core Course | 3 | IoT Architecture, Sensors and Actuators, Communication Protocols, IoT Cloud Platforms, Edge Computing |
| PEC-AIML302G | Departmental Elective-I (Example: Reinforcement Learning) | Professional Elective Course | 3 | Markov Decision Processes, Q-Learning, SARSA Algorithm, Policy Gradient Methods, Deep Reinforcement Learning |
| OEC-CS302G | Open Elective-II (Example: Blockchain Technology) | Open Elective Course | 3 | Distributed Ledger Technology, Cryptography Fundamentals, Consensus Mechanisms, Smart Contracts, Decentralized Applications (DApps) |
| PCC-AIML308G | Data Science Lab | Program Core Course | 1 | Python for Data Analysis, Data Visualization (Matplotlib/Seaborn), Statistical Modeling, Machine Learning Libraries, Case Study Analysis |
| PCC-AIML310G | Natural Language Processing Lab | Program Core Course | 1 | Text Preprocessing Tools, NLTK and SpaCy usage, Word Embedding Models, Sentiment Analysis Implementation, Chatbot Development Basics |
| PCC-AIML312G | Internet of Things Lab | Program Core Course | 1 | Sensor Interfacing, Microcontroller Programming (Arduino/ESP32), Cloud Platform Integration, MQTT/HTTP Communication, IoT Application Development |
| PCC-AIML314G | Departmental Elective-I Lab (Example: Reinforcement Learning Lab) | Professional Elective Course | 1 | Q-Learning Agent Implementation, OpenAI Gym Environments, Policy Gradient Algorithms, Value Iteration, Monte Carlo Methods |
| PCC-AIML316G | Training/Industrial Tour | Program Core Course | 1 | Industry Best Practices, Technological Advancements, Organizational Structure, Professional Etiquette, Report Writing |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC-AIML401G | Block Chain | Program Core Course | 3 | Cryptography in Blockchain, Distributed Ledger Concepts, Hashing Algorithms, Consensus Mechanisms, Smart Contract Development |
| PEC-AIML401G | Departmental Elective-II (Example: AI Ethics) | Professional Elective Course | 3 | Ethical AI Principles, Bias and Fairness in AI, Data Privacy and Security, Explainable AI (XAI), Societal Impact of AI |
| OEC-CS401G | Open Elective-III (Example: Intellectual Property Rights) | Open Elective Course | 3 | Patents, Trademarks, Copyrights, Industrial Designs, Geographical Indications, IPR Enforcement, Digital Rights Management |
| PCC-AIML403G | Project-I | Program Core Course | 8 | Problem Scoping, Literature Review, System Design and Architecture, Incremental Implementation, Mid-Term Evaluation |
| PCC-AIML405G | Block Chain Lab | Program Core Course | 1 | Cryptocurrency Wallets, Smart Contract Deployment (Solidity), DApp Development, Private Blockchain Setup, Transaction Verification |
| PCC-AIML407G | Departmental Elective-II Lab (Example: AI Ethics Lab) | Professional Elective Course | 1 | Bias Detection in Datasets, Fairness Metrics Implementation, Privacy-Preserving AI, Interpretable Model Analysis, Ethical Dilemma Case Studies |
| PCC-AIML409G | Industrial Training | Program Core Course | 0 | Industry Work Experience, Professional Skill Development, Corporate Culture Exposure, Problem-Solving in Real-World Scenarios, Technical Report Writing |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PEC-AIML402G | Departmental Elective-III (Example: Quantum Computing for AI) | Professional Elective Course | 3 | Quantum Mechanics Basics, Qubits and Quantum Gates, Quantum Algorithms (Deutsch-Jozsa, Grover''''s), Quantum Machine Learning, Quantum Hardware Overview |
| PEC-AIML404G | Departmental Elective-IV (Example: AI in Healthcare) | Professional Elective Course | 3 | Medical Image Analysis, Drug Discovery and Development, Disease Diagnosis and Prediction, Personalized Medicine, EHR Data Analysis |
| PCC-AIML402G | Project-II | Program Core Course | 12 | Advanced System Development, Research Methodology, System Optimization and Evaluation, Technical Report and Thesis Writing, Project Defense and Presentation |
| OEC-CS402G | Open Elective-IV (Example: Entrepreneurship Development) | Open Elective Course | 3 | Startup Ecosystem, Business Plan Development, Funding and Venture Capital, Marketing and Sales Strategies, Legal Aspects of Startups |
