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B-TECH in Artificial Intelligence Machine Learning at Rayat Institute of Engineering & Technology
Shahid Bhagat Singh Nagar, Punjab
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About the Specialization
What is Artificial Intelligence & Machine Learning at Rayat Institute of Engineering & Technology Shahid Bhagat Singh Nagar?
This Artificial Intelligence & Machine Learning (AIML) program at Rayat Institute of Engineering & Technology focuses on equipping students with cutting-edge skills in intelligent system design, data analysis, and automation. It addresses the growing demand for AI/ML professionals in the Indian market, emphasizing practical application and theoretical depth. The curriculum is designed to produce innovators capable of solving complex real-world problems using advanced computational techniques and preparing them for the rapidly evolving technological landscape.
Who Should Apply?
This program is ideal for aspiring engineers with a strong aptitude for mathematics, programming, and logical reasoning, seeking entry into the high-growth fields of AI and ML in India. It also caters to individuals looking to upgrade their skills for roles in data science, intelligent automation, and research. Fresh 10+2 graduates with a science background and working professionals aiming for a career transition or advancement in AI are well-suited to leverage this curriculum.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths as AI Engineers, Machine Learning Scientists, Data Scientists, NLP Engineers, or Computer Vision Specialists within India''''s booming tech sector. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning upwards of INR 15-30 LPA. The curriculum prepares students for industry certifications and fosters a strong foundation for higher studies or entrepreneurial ventures in AI.
Student Success Practices
Foundation Stage
Strengthen Core Programming and Math Skills- (Semester 1-2)
Dedicate significant time in Semesters 1-2 to mastering C, Python, and foundational mathematics (Calculus, Linear Algebra, Probability). These are the bedrock for all advanced AI/ML concepts. Regularly solve problems to build logic and problem-solving capabilities.
Tools & Resources
HackerRank, LeetCode, Khan Academy, GeeksforGeeks
Career Connection
A strong foundation ensures easier grasp of complex algorithms, data structures, and statistical models, crucial for cracking technical interviews and excelling in core AI/ML roles.
Engage in Project-Based Learning Early- (Semester 1-2)
Start working on small coding projects related to data manipulation, basic algorithms, or simple data visualization. Utilize online datasets or create your own. This builds practical skills and helps solidify theoretical knowledge, bridging the gap between classroom and real-world application.
Tools & Resources
Kaggle (for datasets), GitHub (for version control), Jupyter Notebooks
Career Connection
Early projects demonstrate practical aptitude and passion, making your resume stand out for internships and entry-level positions in the competitive Indian job market.
Participate in Tech Clubs and Workshops- (Semester 1-2)
Join college technical societies, particularly those focused on programming, data science, or innovation. Attend workshops and seminars to learn new tools and interact with peers and seniors. This fosters a collaborative learning environment and exposes you to current trends.
Tools & Resources
College tech clubs, Local hackathons, Online learning platforms for certifications
Career Connection
Networking and participation in extracurricular tech activities enhance your soft skills, teamwork abilities, and provide opportunities for mentorship, beneficial for future career growth.
Intermediate Stage
Deep Dive into ML/DL Frameworks and Libraries- (Semester 3-5)
Beyond theoretical understanding, get hands-on with Python libraries like Scikit-learn, TensorFlow, and PyTorch. Implement various machine learning algorithms from scratch and then use these frameworks to build and optimize models on real-world datasets.
Tools & Resources
TensorFlow documentation, PyTorch tutorials, Scikit-learn guides, Google Colab
Career Connection
Proficiency in industry-standard ML/DL frameworks is a non-negotiable skill for roles like Machine Learning Engineer, Data Scientist, and AI Developer, especially in India''''s product companies.
Seek Relevant Internships and Industry Projects- (Semester 3-5)
Actively apply for internships or participate in industry-sponsored projects starting from the end of your second year. Focus on roles that offer exposure to data analysis, model building, or AI application development. The 6-8 week industry internship is crucial.
Tools & Resources
LinkedIn, Internshala, College placement cell, Company career pages
Career Connection
Internships provide invaluable practical experience, industry contacts, and often lead to pre-placement offers, significantly boosting your employability in the Indian tech landscape.
Build a Strong Portfolio of AI/ML Projects- (Semester 3-5)
Develop diverse projects demonstrating your skills in different AI/ML domains (e.g., a sentiment analysis tool, an image classifier, a recommendation system). Document your work meticulously on GitHub and write clear project reports.
Tools & Resources
GitHub, Personal website/blog, Medium (for technical articles)
Career Connection
A robust project portfolio is your strongest asset for showcasing capabilities to potential employers in India, especially for roles requiring practical implementation skills.
Advanced Stage
Specialize and Engage in Advanced Research- (Semester 6-8)
In your final year, choose electives that align with your career interests (e.g., NLP, Computer Vision, Reinforcement Learning). For your major project, aim for novelty and address a challenging problem, potentially contributing to academic publications or open-source initiatives.
Tools & Resources
ArXiv, Google Scholar, ResearchGate, Conferences (e.g., AAAI, NeurIPS)
Career Connection
Specialized knowledge and research experience are highly valued for advanced roles, R&D positions, and academic pursuits in AI/ML, giving you a competitive edge.
Prepare Rigorously for Placements and Higher Education- (Semester 6-8)
Focus on interview preparation, including mock interviews, behavioral questions, and revising core computer science and AI/ML concepts. If pursuing higher studies, prepare for competitive exams like GATE or GRE/TOEFL and work on strong Statements of Purpose.
Tools & Resources
InterviewBit, Glassdoor, College placement cell workshops, Test preparation materials
Career Connection
Effective preparation is key to securing top placements in Indian companies or gaining admission to prestigious universities for Master''''s or PhD programs, accelerating your career trajectory.
Develop Ethical AI Awareness and Leadership Skills- (Semester 6-8)
Actively engage with discussions on ethical AI, bias, privacy, and responsible AI development. Take on leadership roles in team projects or student organizations to hone managerial and communication skills, which are critical for senior roles.
Tools & Resources
AI Ethics courses (e.g., from Coursera), Industry reports on responsible AI, Team projects
Career Connection
Understanding AI ethics is increasingly important for leadership roles in Indian tech, ensuring you can guide future AI development responsibly and effectively within organizational contexts.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 Examination with Physics and Mathematics as compulsory subjects along with one of Chemistry / Biotechnology / Biology / Technical Vocational subject. Obtained at least 45% marks (40% for reserved category) in these subjects. OR Passed Diploma in Engineering and Technology examination with at least 45% marks (40% for reserved category).
Duration: 8 semesters / 4 years
Credits: 139 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC 101-23 | Engineering Physics | Core | 4 | Wave Optics and Interference, Quantum Mechanics and Matter Waves, Solid State Physics and X-rays, Lasers and Fibre Optics, Semiconductor Physics and Devices |
| ESC 101-23 | Basic Electrical Engineering | Core | 4 | DC and AC Circuits, Transformers and Induction Motors, DC Machines and Synchronous Machines, Electrical Wiring and Safety, Basic Electronic Components |
| HSMC 101-23 | English Communication Skills | Humanities and Social Sciences | 2 | Reading Comprehension and Vocabulary, Grammar and Writing Skills, Public Speaking and Presentation, Listening Skills and Group Discussion, Professional Communication |
| ESC 102-23 | Programming for Problem Solving | Core | 3 | Introduction to C Programming, Data Types, Operators, Control Structures, Functions, Arrays, Strings, Pointers and Structures, File Handling and Preprocessors |
| BSC 102-23 | Engineering Physics Lab | Lab | 1 | Compound Pendulum and Torsional Pendulum, Newton''''s Rings and Diffraction Grating, Semiconductor Diode Characteristics, Laser Characteristics, Optical Fiber Numerical Aperture |
| ESC 103-23 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Kirchhoff''''s Laws, Superposition and Thevenin''''s Theorem, Three-Phase AC Circuits, Transformer Load Test, Motor Speed Control |
| HSMC 102-23 | English Communication Skills Lab | Lab | 1 | Phonetics and Pronunciation, Extempore and Presentation Practice, Group Discussion and Role Play, Interview Skills and Resume Writing, Debate and Public Speaking |
| ESC 104-23 | Programming for Problem Solving Lab | Lab | 2 | Conditional Statements and Loops, Array Manipulation and String Operations, Functions and Recursion, Pointers and Dynamic Memory Allocation, Structures and File Handling |
| MC 101-23 | Environmental Science | Mandatory Non-Credit | 0 | Ecosystems and Biodiversity, Natural Resources and Conservation, Environmental Pollution and Control, Waste Management and Climate Change, Environmental Ethics and Policies |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC 103-23 | Engineering Chemistry | Core | 4 | Water Treatment and Analysis, Corrosion and its Control, Polymers and Composites, Fuels and Combustion, Lubricants and Adhesives |
| BSC 104-23 | Mathematics-I (Linear Algebra and Calculus) | Core | 4 | Matrices and Determinants, Eigenvalues and Eigenvectors, Differential Calculus and Applications, Integral Calculus and Multiple Integrals, Sequences, Series and Power Series |
| ESC 105-23 | Engineering Graphics and Design | Core | 3 | Introduction to Engineering Graphics, Orthographic Projections, Isometric Projections, Sections of Solids and Developments, Introduction to AutoCAD |
| ESC 106-23 | Workshop Manufacturing Practices | Lab | 2 | Carpentry and Fitting Shop, Welding and Sheet Metal Shop, Machine Shop and Foundry Shop, Forging and Smithy Shop, Plumbing and Electrical Shop |
| BSC 105-23 | Engineering Chemistry Lab | Lab | 1 | Water Hardness Determination, Acid-Base Titrations, Viscosity and Surface Tension, Polymer Synthesis and Characterization, Fuel Analysis |
| HSMC 103-23 | NSS/NCC/Physical Education | Mandatory Non-Credit | 0 | Community Service Initiatives (NSS), Discipline and Patriotism (NCC), Physical Fitness and Sports, Teamwork and Leadership, Social Awareness Programs |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC 201-23 | Mathematics-II (Probability and Statistics) | Core | 4 | Probability Theory and Axioms, Random Variables and Distributions, Joint Probability Distributions, Sampling Theory and Estimation, Regression and Correlation Analysis |
| PCC CS-301-23 | Data Structures | Core | 3 | Arrays, Linked Lists, Stacks, Queues, Trees and Binary Search Trees, Graphs and Graph Traversal Algorithms, Sorting Algorithms (Merge, Quick, Heap), Searching Algorithms and Hashing |
| PCC CS-302-23 | Object Oriented Programming | Core | 3 | Classes, Objects, Constructors, Destructors, Inheritance and Polymorphism, Abstraction and Encapsulation, Virtual Functions and Abstract Classes, Exception Handling and File I/O |
| PCC CS-303-23 | Computer Organization and Architecture | Core | 3 | Basic Computer Functions and Bus Structure, CPU Organization and Instruction Set, Memory Organization and Hierarchy, I/O Organization and Interrupts, Pipelining and Parallel Processing |
| PCC CS-304-23 | Data Structures Lab | Lab | 2 | Implementation of Linked Lists and Stacks, Implementation of Queues and Trees, Graph Traversal Algorithms (BFS, DFS), Sorting Algorithms (Bubble, Insertion, Selection), Hashing Techniques and Collision Resolution |
| PCC CS-305-23 | Object Oriented Programming Lab | Lab | 2 | Classes and Objects in C++ / Java, Inheritance and Function Overloading, Polymorphism and Virtual Functions, Templates and Generic Programming, Exception Handling and Multithreading |
| PCC CS-306-23 | IT Workshop (Python/R) | Lab | 2 | Python Fundamentals and Data Types, Control Flow and Functions in Python, Libraries for Data Manipulation (Numpy, Pandas), Data Visualization (Matplotlib, Seaborn), Introduction to R Programming |
| MC 201-23 | Constitution of India | Mandatory Non-Credit | 0 | Preamble and Fundamental Rights, Directive Principles of State Policy, Structure and Functions of Union Government, State Government and Local Administration, Constitutional Amendments and Emergency Provisions |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC AIML-401-23 | Discrete Mathematics | Core | 4 | Set Theory and Logic, Relations and Functions, Group Theory and Rings, Graph Theory and Trees, Combinatorics and Probability |
| PCC CS-401-23 | Operating Systems | Core | 3 | Process Management and Scheduling, Thread Management and Concurrency, Memory Management Techniques, File Systems and I/O Management, Deadlocks and Synchronization |
| PCC CS-402-23 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis and Asymptotic Notations, Divide and Conquer Algorithms, Greedy Algorithms and Dynamic Programming, Graph Algorithms (MST, Shortest Path), Backtracking and Branch & Bound |
| PCC AIML-402-23 | Artificial Intelligence | Core | 3 | Introduction to AI and Intelligent Agents, Search Algorithms (DFS, BFS, A*), Knowledge Representation and Reasoning, Planning and Uncertainty, Introduction to Machine Learning and Robotics |
| PCC CS-403-23 | Operating Systems Lab | Lab | 2 | Linux Commands and Shell Scripting, Process Creation and Inter-process Communication, CPU Scheduling Algorithms Implementation, Memory Allocation Strategies, File System Operations |
| PCC CS-404-23 | Design and Analysis of Algorithms Lab | Lab | 2 | Implementation of Sorting and Searching Algorithms, Dynamic Programming Solutions, Graph Traversal and Shortest Path Algorithms, Greedy Algorithms Implementation, Backtracking and Branch & Bound Problems |
| PCC AIML-403-23 | Artificial Intelligence Lab | Lab | 2 | Implementing Search Algorithms (DFS, BFS, A*), Constraint Satisfaction Problems, Knowledge Representation using Prolog/Python, Decision Tree Implementation, Introduction to NLP tasks |
| HSMC 201-23 | Human Values | Mandatory Non-Credit | 0 | Self-Exploration and Right Understanding, Harmony in the Family and Society, Professional Ethics and Values, Universal Human Values, Holistic Development |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC AIML-501-23 | Database Management Systems | Core | 3 | ER Model and Relational Model, Relational Algebra and Calculus, SQL Queries and Database Design, Normalization and Dependency Theory, Transaction Management and Concurrency Control |
| PCC AIML-502-23 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Model Evaluation and Validation, Ensemble Methods (Bagging, Boosting), Support Vector Machines and Decision Trees |
| PCC CS-501-23 | Computer Networks | Core | 3 | Network Topologies and OSI/TCP-IP Model, Physical Layer and Data Link Layer, Network Layer (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, DNS) |
| PEC CS-501-23 | Professional Elective - I (Advanced Data Structures) | Elective | 3 | Advanced Trees (AVL, Red-Black, B-Trees), Heaps and Priority Queues, Disjoint Set Union, Segment Trees and Fenwick Trees, Tries and Suffix Arrays |
| PEC CS-502-23 | Professional Elective - I (Digital Image Processing) | Elective | 3 | Image Fundamentals and Transformations, Image Enhancement and Restoration, Image Segmentation and Representation, Color Image Processing, Wavelets and Multi-resolution Processing |
| PEC CS-503-23 | Professional Elective - I (Software Engineering) | Elective | 3 | Software Development Life Cycle Models, Software Requirements Engineering, Software Design Principles, Software Testing and Maintenance, Software Project Management |
| PEC CS-504-23 | Professional Elective - I (Computer Graphics) | Elective | 3 | Graphics Primitives and Rasterization, 2D and 3D Transformations, Clipping and Projections, Color Models and Shading, Rendering and Animation |
| OEC CS-501-23 | Open Elective - I (Data Analytics) | Elective | 3 | Data Collection and Cleaning, Exploratory Data Analysis, Statistical Methods for Data Analysis, Data Visualization Techniques, Introduction to Big Data |
| OEC CS-502-23 | Open Elective - I (Web Technologies) | Elective | 3 | HTML, CSS, JavaScript Fundamentals, Client-Side Scripting and Frameworks, Server-Side Programming (Node.js/Python), Database Connectivity and Web Services, Web Security and Deployment |
| OEC CS-503-23 | Open Elective - I (Cyber Security Fundamentals) | Elective | 3 | Introduction to Cyber Security, Network Security and Cryptography, Malware and Attack Vectors, Web Application Security, Security Policies and Incident Response |
| PCC AIML-503-23 | Database Management Systems Lab | Lab | 2 | DDL and DML Commands in SQL, Advanced SQL Queries (Joins, Subqueries), PL/SQL Programming and Stored Procedures, Database Design and Normalization, Introduction to NoSQL Databases |
| PCC AIML-504-23 | Machine Learning Lab | Lab | 2 | Python for Machine Learning (Scikit-learn), Implementing Regression Models, Implementing Classification Algorithms (SVM, KNN), Clustering Techniques (K-Means, Hierarchical), Model Evaluation and Hyperparameter Tuning |
| PCC CS-502-23 | Computer Networks Lab | Lab | 2 | Network Cable Crimping and Configuration, Packet Analysis using Wireshark, Socket Programming (TCP, UDP), Routing Protocols Implementation, Network Security Configuration |
| PCC CS-503-23 | Project Based Learning | Project | 1 | Problem Identification and Scoping, Literature Survey and Research, System Design and Implementation, Testing and Evaluation, Report Writing and Presentation |
| SODE-101-23 | Soft Skills and Aptitude | Mandatory Non-Credit | 0 | Communication Skills and Body Language, Teamwork and Leadership, Problem Solving and Critical Thinking, Quantitative Aptitude and Logical Reasoning, Interview Preparation and Group Discussion |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCC AIML-601-23 | Deep Learning | Core | 3 | Artificial Neural Networks and Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Deep Learning Frameworks (TensorFlow/PyTorch), Transfer Learning and Fine-tuning |
| PCC AIML-602-23 | Natural Language Processing | Core | 3 | Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), Sequence Models (HMMs, CRFs), Text Classification and Sentiment Analysis, Machine Translation and Chatbots |
| HSMC 301-23 | Entrepreneurship & Start-ups | Humanities and Social Sciences | 2 | Concept of Entrepreneurship and Innovation, Business Idea Generation and Validation, Business Plan Development, Startup Funding and Legal Aspects, Marketing and Growth Strategies |
| PEC CS-601-23 | Professional Elective - II (Compiler Design) | Elective | 3 | Phases of a Compiler, Lexical Analysis and Finite Automata, Syntax Analysis and Parsing Techniques, Intermediate Code Generation, Code Optimization and Code Generation |
| PEC CS-602-23 | Professional Elective - II (Cloud Computing) | Elective | 3 | Cloud Computing Architecture and Models (IaaS, PaaS, SaaS), Virtualization Technologies, Cloud Security and Data Privacy, Cloud Service Providers (AWS, Azure, GCP), Cloud Storage and Networking |
| PEC CS-603-23 | Professional Elective - II (Software Project Management) | Elective | 3 | Software Project Planning and Estimation, Project Scheduling and Tracking, Risk Management and Quality Management, Software Configuration Management, Agile Project Management |
| PEC CS-604-23 | Professional Elective - II (Big Data Analytics) | Elective | 3 | Introduction to Big Data and Hadoop Ecosystem, HDFS and MapReduce, Spark and Stream Processing, NoSQL Databases (Cassandra, MongoDB), Data Warehousing and Data Mining for Big Data |
| OEC CS-601-23 | Open Elective - II (IoT (Internet of Things)) | Elective | 3 | Introduction to IoT Architecture, IoT Devices and Sensors, Communication Protocols (MQTT, CoAP), IoT Data Analytics and Cloud Platforms, IoT Security and Applications |
| OEC CS-602-23 | Open Elective - II (Augmented and Virtual Reality) | Elective | 3 | Introduction to AR/VR Concepts, VR Devices and Technologies, AR Applications and Development, 3D Graphics and Interaction Techniques, Challenges and Future of AR/VR |
| OEC CS-603-23 | Open Elective - II (Blockchain Technology) | Elective | 3 | Fundamentals of Blockchain and Cryptography, Distributed Ledger Technology, Consensus Mechanisms (PoW, PoS), Smart Contracts and Ethereum, Blockchain Applications and Challenges |
| PCC AIML-603-23 | Deep Learning Lab | Lab | 2 | Building ANNs using TensorFlow/Keras, Implementing CNNs for Image Classification, Developing RNNs for Sequence Prediction, Hyperparameter Tuning and Regularization, Working with Pre-trained Models |
| PCC AIML-604-23 | Natural Language Processing Lab | Lab | 2 | Text Preprocessing using NLTK, Implementing Word Embeddings, Text Classification with Machine Learning Models, Sentiment Analysis on Text Data, Building a Simple Chatbot |
| PROJ-CS-601-23 | Mini Project | Project | 2 | Problem Definition and Literature Review, System Design and Module Development, Implementation using Relevant Technologies, Testing, Debugging, and Documentation, Project Report and Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PEC AIML-701-23 | Professional Elective - III (Reinforcement Learning) | Elective | 3 | Markov Decision Processes, Dynamic Programming in RL, Monte Carlo and Temporal-Difference Learning, Q-Learning and SARSA, Deep Reinforcement Learning |
| PEC AIML-702-23 | Professional Elective - III (Computer Vision) | Elective | 3 | Image Formation and Filtering, Feature Detection and Matching, Object Recognition and Classification, Image Segmentation and Tracking, Deep Learning for Computer Vision |
| PEC AIML-703-23 | Professional Elective - III (Robotics Process Automation (RPA)) | Elective | 3 | Introduction to RPA and its Benefits, RPA Tools (UiPath, Automation Anywhere), Process Mapping and Automation Design, Bot Development and Deployment, RPA Security and Governance |
| PEC AIML-704-23 | Professional Elective - III (Big Data for AI/ML) | Elective | 3 | Big Data Technologies for AI/ML, Distributed Computing (Spark, Hadoop), Data Lake and Data Warehousing, Real-time Data Processing for AI, Scalable ML Pipelines |
| PEC AIML-705-23 | Professional Elective - IV (Explainable AI) | Elective | 3 | Introduction to XAI and its Importance, Local and Global Explanation Methods, Feature Importance (SHAP, LIME), Model Interpretability Techniques, Ethical Considerations in XAI |
| PEC AIML-706-23 | Professional Elective - IV (Time Series Analysis) | Elective | 3 | Time Series Components and Decomposition, ARIMA and SARIMA Models, Forecasting Techniques, Spectral Analysis of Time Series, Machine Learning for Time Series |
| PEC AIML-707-23 | Professional Elective - IV (Bio-inspired AI) | Elective | 3 | Evolutionary Algorithms (Genetic Algorithms), Swarm Intelligence (PSO, ACO), Artificial Immune Systems, Neural Networks as Bio-inspired Models, Fuzzy Logic and Rough Sets |
| PEC AIML-708-23 | Professional Elective - IV (Ethics in AI) | Elective | 3 | Ethical Dilemmas in AI Development, Bias and Fairness in AI Systems, Privacy and Data Protection, Accountability and Transparency in AI, Societal Impact of AI |
| OEC CS-701-23 | Open Elective - III (Digital Marketing) | Elective | 3 | Search Engine Optimization (SEO), Social Media Marketing, Content Marketing and Strategy, Email Marketing and Analytics, Pay-Per-Click (PPC) Advertising |
| OEC CS-702-23 | Open Elective - III (Game Development) | Elective | 3 | Game Design Principles, Game Engines (Unity, Unreal), Programming for Games (C#, C++), Graphics and Physics in Games, Game Monetization and Publishing |
| OEC CS-703-23 | Open Elective - III (Intellectual Property Rights) | Elective | 3 | Introduction to IPR and its Importance, Patents, Copyrights, and Trademarks, Industrial Designs and Geographical Indications, Protection of Trade Secrets, IPR in Digital World and Software |
| PROJ-CS-701-23 | Industrial Training (6 Months) / Project Work | Project | 10 | Real-world Problem Solving, Industry-specific Tool Proficiency, System Design and Development, Testing and Deployment, Comprehensive Technical Report |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PROJ-CS-801-23 | Project Work | Project | 8 | Advanced Research and Development, Innovative System Prototyping, Performance Evaluation and Optimization, Technical Documentation and Publication, Oral Presentation and Viva-Voce |
| SODE-201-23 | Universal Human Values | Mandatory Non-Credit | 0 | Understanding Human Values and Ethics, Harmony in the Family and Society, Professional Ethics and Code of Conduct, Relationship between Technology and Human Values, Holistic Development and Living |
