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B-TECH in Computer Science Engineering Artificial Intelligence at School of Technology
Gandhinagar, Gujarat
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About the Specialization
What is Computer Science & Engineering (Artificial Intelligence) at School of Technology Gandhinagar?
This B.Tech Computer Science & Engineering (Artificial Intelligence & Machine Learning) program at Pandit Deendayal Energy University (PDEU) focuses on equipping students with a profound understanding of intelligent systems, data analysis, and advanced algorithms. With a strong emphasis on core AI, machine learning, deep learning, and natural language processing, the curriculum is designed to meet the growing demand for skilled professionals in India''''s rapidly expanding technology sector. The program differentiates itself by integrating theoretical foundations with hands-on project-based learning, preparing graduates for cutting-edge roles.
Who Should Apply?
This program is ideal for aspiring engineers who have a strong aptitude for mathematics, logical reasoning, and an insatiable curiosity about how intelligent systems work. It caters to fresh graduates seeking entry into the high-demand fields of artificial intelligence, data science, and machine learning. Working professionals from related disciplines looking to upskill in AI/ML, or career changers aiming to transition into the data-driven industry, will also find the comprehensive curriculum beneficial, provided they meet the foundational prerequisites.
Why Choose This Course?
Graduates of this program can expect diverse and rewarding career paths in the Indian job market, including roles such as AI Engineer, Machine Learning Scientist, Data Scientist, NLP Specialist, and Computer Vision Engineer across IT, finance, healthcare, and automotive sectors. Entry-level salaries in India typically range from INR 4-8 LPA, with experienced professionals commanding INR 15+ LPA depending on skills and company. The program also aligns with global professional certifications, fostering growth trajectories in both national and multinational companies.
Student Success Practices
Foundation Stage
Master Programming & Math Fundamentals- (Semester 1-2)
Establish a strong foundation in programming languages like C and Python, alongside essential mathematical concepts such as Calculus, Linear Algebra, and Discrete Mathematics. Regularly practice coding challenges and solve mathematical problems to solidify understanding. This strong academic base is crucial for grasping complex AI/ML algorithms and excelling in advanced subjects.
Tools & Resources
HackerRank, LeetCode, NPTEL courses for Math & Programming
Career Connection
A robust foundation translates directly to better performance in technical interviews, problem-solving in projects, and quicker adaptation to new technologies in future roles.
Develop Strong Problem-Solving Skills- (Semester 1-2)
Actively participate in coding contests and engage in solving algorithmic puzzles outside coursework. Focus on understanding data structures and algorithms thoroughly. Collaborate with peers to discuss different approaches to problems. This enhances logical thinking and efficient coding, which are core requirements for any AI/ML role.
Tools & Resources
CodeChef, GeeksforGeeks, Online Judge platforms
Career Connection
Exceptional problem-solving skills are highly valued by employers and are key to cracking coding rounds and technical assessments during placement drives.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups with classmates to discuss challenging concepts, review assignments, and prepare for exams. Actively participate in departmental technical clubs or societies. Teaching and explaining concepts to peers deepens your own understanding and builds a strong academic support network.
Tools & Resources
Discord/WhatsApp groups for study, Departmental technical clubs
Career Connection
Develops teamwork, communication skills, and critical thinking, all essential for collaborating effectively in professional engineering teams.
Intermediate Stage
Build Foundational AI/ML Projects- (Semester 3-5)
Start applying theoretical knowledge from Data Structures, OOP, AI, and Machine Learning to small, practical projects. Utilize publicly available datasets (e.g., Kaggle) to build classification or regression models. Focus on understanding the entire project lifecycle from data collection to model deployment. This helps in building a portfolio.
Tools & Resources
Kaggle, GitHub, Jupyter Notebooks, Scikit-learn
Career Connection
Practical projects demonstrate your applied skills, which are crucial for securing internships and entry-level positions in AI/ML companies. A strong project portfolio sets you apart.
Seek Early Industry Exposure (Internships/Workshops)- (Semester 3-5)
Actively look for short-term internships, workshops, or industry visits related to AI/ML during summer breaks. Attend guest lectures and webinars by industry professionals. This provides real-world context, helps identify areas of interest, and builds an early professional network. Even short stints provide invaluable experience.
Tools & Resources
Internshala, LinkedIn, University career fairs
Career Connection
Early exposure helps you understand industry demands, refine your career goals, and makes you a more attractive candidate for future placements. It’s vital for transitioning from academia to industry.
Specialize in AI/ML Tools & Libraries- (Semester 3-5)
Get hands-on experience with core Python libraries essential for AI/ML, such as NumPy, Pandas, Matplotlib, Scikit-learn, and critically, Deep Learning frameworks like TensorFlow or PyTorch. Complete online certification courses focused on these tools to gain proficiency. This practical skill set is highly sought after by employers.
Tools & Resources
Coursera/edX for specialized courses, Official documentation for libraries, Google Colab
Career Connection
Proficiency in industry-standard tools and libraries is a direct requirement for most AI/ML engineering roles, enabling you to contribute effectively from day one.
Advanced Stage
Undertake Research/Major Projects in AI/ML- (Semester 6-8)
Focus your final year major project on a significant, challenging problem within Deep Learning, NLP, or Computer Vision. Aim for innovative solutions, publish research papers in college journals or present at internal conferences. This showcases advanced technical expertise and research capabilities.
Tools & Resources
Access to university research labs, Advanced Deep Learning frameworks, Academic databases
Career Connection
A strong major project can serve as a key differentiator for placements in R&D roles, startups, or for admission to prestigious postgraduate programs in India and abroad.
Prepare for Placements & Advanced Interviews- (Semester 6-8)
Dedicatedly practice advanced data structures, algorithms, system design, and AI/ML-specific concepts for technical interviews. Participate in mock interviews, resume-building workshops, and career counseling sessions. Focus on behavioral questions and demonstrating problem-solving thought processes. Prepare a strong portfolio of projects.
Tools & Resources
LeetCode Hard problems, GeeksforGeeks interview archives, PDPU Career Development Cell
Career Connection
Effective interview preparation is paramount for securing desirable placements as an AI/ML Engineer, Data Scientist, or Software Developer in top-tier companies.
Network with Industry Leaders & Alumni- (Semester 6-8)
Actively engage with industry professionals and alumni through LinkedIn, conferences, seminars, and university networking events. Seek mentorship opportunities and learn about career trajectories and emerging trends. Building a strong professional network can open doors to new opportunities, referrals, and career guidance.
Tools & Resources
LinkedIn, Industry conferences (e.g., Data Science Summit), Alumni association events
Career Connection
Networking is crucial for career advancement, uncovering hidden job markets, and gaining insights that are invaluable for long-term professional growth and leadership development.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 or equivalent 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 the above subjects taken together. Must have appeared in JEE (Main) examination for the current academic year.
Duration: 8 semesters (4 years)
Credits: 164 Credits
Assessment: Internal: undefined, External: undefined
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CP101T | Calculus | Core | 3 | Functions and Limits, Differentiation and Applications, Integration Techniques, Multiple Integrals, Vector Calculus |
| 22CP102T | Applied Physics | Core | 3 | Wave Optics and Interference, Lasers and Fiber Optics, Quantum Mechanics Introduction, Solid State Physics, Nanomaterials and Applications |
| 22CP103T | Fundamentals of Electrical & Electronics Engineering | Core | 3 | DC and AC Circuits, Transformers and Electrical Machines, Semiconductor Diodes, Transistors and Amplifiers, Operational Amplifiers, Digital Electronics |
| 22CP104T | Environmental Studies | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Waste Management, Environmental Legislation and Ethics |
| 22CP105T | Programming for Problem Solving | Core | 3 | Introduction to Programming (C), Data Types, Operators, Expressions, Control Flow Statements, Functions, Arrays, Pointers, Strings and File I/O |
| 22CP106L | Programming for Problem Solving Laboratory | Lab | 2 | C Programming Exercises, Conditional and Looping Constructs, Function Implementation, Array and Pointer Usage, File Operations in C |
| 22CP107L | Applied Physics Laboratory | Lab | 1 | Optics Experiments, Laser Characteristics, Semiconductor Device Measurements, Magnetic Field Studies, Measurement Techniques |
| 22CP108L | Fundamentals of Electrical & Electronics Engineering Laboratory | Lab | 1 | Basic Circuit Laws (Ohm''''s, Kirchhoff''''s), Diode and Transistor Characteristics, Rectifiers and Filters, Digital Logic Gates, Basic Electronic Component Testing |
| 22CP109L | Engineering Graphics & Design | Lab | 2 | Orthographic Projections, Sectional Views, Isometric Projections, CAD Software Introduction, Assembly Drawing Principles |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CP201T | Differential Equations & Linear Algebra | Core | 3 | First Order Differential Equations, Higher Order Differential Equations, Laplace Transforms, Vector Spaces and Subspaces, Matrices and Eigenvalues, Linear Transformations |
| 22CP202T | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry and Corrosion, Fuels and Combustion, Polymers and Composites, Spectroscopic Techniques |
| 22CP203T | Data Structures | Core | 3 | Arrays and Pointers, Stacks and Queues, Linked Lists, Trees and Binary Search Trees, Graphs and Graph Algorithms, Searching and Sorting |
| 22CP204T | Basics of Mechanical Engineering | Core | 3 | Thermodynamics Principles, IC Engines and Power Plants, Refrigeration and Air Conditioning, Power Transmission Systems, Manufacturing Processes |
| 22HS201T | Communication Skills | Core | 2 | Listening and Speaking Skills, Reading Comprehension, Writing Business Correspondence, Presentation Techniques, Group Discussion and Interview Skills |
| 22CP205L | Engineering Chemistry Laboratory | Lab | 1 | Volumetric Analysis, Instrumental Analysis Techniques, Water Quality Parameters, Viscosity and Surface Tension, Corrosion Rate Measurement |
| 22CP206L | Data Structures Laboratory | Lab | 2 | Implementation of Stacks and Queues, Linked List Operations, Tree Traversal Algorithms, Graph Algorithms Implementation, Sorting and Searching Algorithms |
| 22CP207L | Basics of Mechanical Engineering Laboratory | Lab | 1 | IC Engine Performance Test, Refrigeration Cycle Analysis, Workshop Practice (Machining, Welding), Fluid Mechanics Experiments, Material Testing |
| 22CP208L | Professional Skills & Values | Lab | 2 | Teamwork and Collaboration, Ethics and Professionalism, Effective Communication, Time Management, Leadership Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CP301T | Discrete Mathematics | Core | 3 | Set Theory and Logic, Relations and Functions, Graph Theory Fundamentals, Combinatorics and Counting, Algebraic Structures |
| 22CP302T | Digital Logic & Design | Core | 3 | Boolean Algebra and Logic Gates, Combinational Logic Circuits, Sequential Logic Circuits, Registers and Counters, Memory Elements |
| 22CP303T | Object-Oriented Programming (C++) | Core | 3 | Classes and Objects, Inheritance and Polymorphism, Abstraction and Encapsulation, Constructors and Destructors, Exception Handling, File I/O |
| 22CP304T | Database Management Systems | Core | 3 | DBMS Architecture and Models, ER Model and Relational Model, Structured Query Language (SQL), Normalization, Transaction Management and Concurrency Control |
| 22CP305T | Computer Architecture & Organization | Core | 3 | CPU Organization, Instruction Set Architecture, Pipelining and Parallel Processing, Memory Hierarchy, Input/Output Organization |
| 22CP306L | Digital Logic & Design Laboratory | Lab | 1 | Logic Gate Implementation, Combinational Circuit Design, Sequential Circuit Design, Flip-Flops and Latches, Registers and Counters Experiments |
| 22CP307L | Object-Oriented Programming (C++) Laboratory | Lab | 2 | Class and Object Implementation, Inheritance and Polymorphism Programs, Operator Overloading, File Handling in C++, Template Programming |
| 22CP308L | Database Management Systems Laboratory | Lab | 2 | SQL DDL and DML Commands, Complex Queries and Joins, Database Schema Design, Stored Procedures and Triggers, Data Manipulation using SQL |
| 22CP309P | Project Based Learning – I | Project | 1 | Problem Identification, Literature Review, Project Planning, Basic Implementation, Report Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CP401T | Operating Systems | Core | 3 | Process Management and Scheduling, Memory Management, File Systems, I/O Systems, Deadlocks and Concurrency Control |
| 22CP402T | Design & Analysis of Algorithms | Core | 3 | Algorithm Analysis and Complexity, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms, NP-Completeness |
| 22CP403T | Artificial Intelligence | Core | 3 | Introduction to AI Agents, Search Algorithms (DFS, BFS, A*), Knowledge Representation and Reasoning, Game Playing (Minimax), Machine Learning Basics, Expert Systems |
| 22CP404T | Python Programming | Core | 3 | Python Language Fundamentals, Data Structures in Python, Functions and Modules, Object-Oriented Programming in Python, File I/O and Exception Handling, Introduction to Libraries (Numpy, Pandas) |
| 22CP405T | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions and Languages, Context-Free Grammars, Pushdown Automata, Turing Machines and Computability, Undecidability |
| 22CP406L | Operating Systems Laboratory | Lab | 1 | Shell Scripting, Process Creation and Management, CPU Scheduling Algorithms, Memory Management Techniques, Synchronization Problems |
| 22CP407L | Design & Analysis of Algorithms Laboratory | Lab | 2 | Implementation of Sorting Algorithms, Graph Traversal Algorithms, Dynamic Programming Solutions, Greedy Algorithm Problems, Time and Space Complexity Analysis |
| 22CP408L | Artificial Intelligence Laboratory | Lab | 2 | Implementing Search Algorithms, Logic Programming (Prolog), Simple Expert Systems, Game Playing Implementations, Introduction to AI Libraries |
| 22CP409L | Python Programming Laboratory | Lab | 2 | Basic Python Programs, Data Structures in Python, Functions and Modules Usage, Object-Oriented Python Projects, File Handling and Exception Management |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CP501T | Computer Networks | Core | 3 | Network Models (OSI, TCP/IP), Physical and Data Link Layers, Network Layer Protocols (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, DNS), Network Security Basics |
| 22CP502T | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Validation, Decision Trees, SVMs, k-NN, Ensemble Methods, Introduction to Neural Networks |
| 22CP503T | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization, Runtime Environment |
| 22CP504T | Advanced Database Management Systems | Core | 3 | Distributed Databases, Object-Oriented Databases, Data Warehousing Concepts, Data Mining Techniques, Big Data Introduction, NoSQL Databases |
| 22CPAI01T | Natural Language Processing | Core | 3 | NLP Fundamentals and History, Text Preprocessing and Tokenization, N-grams and Language Models, Part-of-Speech Tagging, Parsing and Syntactic Analysis, Semantic Analysis and Word Embeddings |
| 22CP505L | Computer Networks Laboratory | Lab | 1 | Network Configuration and Troubleshooting, Socket Programming, Network Protocol Implementation, Packet Sniffing and Analysis, Client-Server Communication |
| 22CP506L | Machine Learning Laboratory | Lab | 2 | Data Preprocessing with Pandas, Implementing Regression Models, Classification Algorithm Implementations, Clustering Techniques, Model Evaluation and Hyperparameter Tuning |
| 22CPAI02L | Natural Language Processing Laboratory | Lab | 2 | NLTK and SpaCy Usage, Text Classification Tasks, Sentiment Analysis, Named Entity Recognition, Machine Translation Basics |
| 22CP507P | Project Based Learning – II | Project | 2 | Advanced Problem Definition, System Design and Architecture, Implementation with Chosen Technologies, Testing and Debugging, Project Documentation and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CPAI03T | Deep Learning | Core | 3 | Artificial Neural Networks Fundamentals, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs and Transformers, Deep Learning Frameworks (TensorFlow/PyTorch) |
| 22CPAI04T | Data Mining & Data Warehousing | Core | 3 | Data Preprocessing and Cleaning, Association Rule Mining, Classification Algorithms, Clustering Techniques, Data Warehousing Architecture (OLAP), Dimensional Modeling |
| 22CP601T | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing Techniques, Software Project Management, Agile Methodologies |
| 22CP602T | Cryptography & Network Security | Core | 3 | Symmetric Key Cryptography, Asymmetric Key Cryptography, Hash Functions and Digital Signatures, Network Security Protocols (SSL/TLS, IPsec), Firewalls and Intrusion Detection Systems |
| Program Elective – I | Program Elective – I (e.g., Cloud Computing, Big Data Analytics, IoT) | Elective | 3 | Advanced topics in chosen elective field, Implementation of modern technologies, System design considerations, Real-world applications, Emerging trends |
| 22CPAI05L | Deep Learning Laboratory | Lab | 2 | Implementing Neural Networks, CNNs for Image Classification, RNNs for Sequence Data, TensorFlow/PyTorch Projects, Hyperparameter Tuning in Deep Learning |
| 22CPAI06L | Data Mining & Data Warehousing Laboratory | Lab | 2 | Using Data Mining Tools (e.g., Weka), Implementing Classification Algorithms, Clustering and Association Rule Mining, Building Data Cubes and OLAP Operations, Extracting Insights from Large Datasets |
| 22CP603L | Software Engineering Laboratory | Lab | 1 | UML Diagramming, Requirements Gathering Tools, Software Design Patterns Implementation, Testing Frameworks, Version Control Systems |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CPAI07T | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming in RL, Monte Carlo Methods, Temporal Difference Learning (Q-learning), Policy Gradient Methods, Deep Reinforcement Learning |
| 22CPAI08T | Computer Vision | Core | 3 | Image Processing Fundamentals, Feature Detection and Extraction, Object Recognition and Detection, Image Segmentation, Deep Learning for Computer Vision, Applications of Computer Vision |
| Program Elective – II | Program Elective – II (e.g., Blockchain Technologies, Robotics, Information Retrieval) | Elective | 3 | Specialized concepts in chosen field, Advanced methodologies and tools, Case studies and industry applications, Problem-solving in specific domains, Research frontiers |
| Open Elective – I | Open Elective – I | Elective | 3 | Interdisciplinary subject chosen by student, Introduction to new domains, Skill development outside core specialization, Broadening academic perspective, General knowledge enrichment |
| 22CP701P | Project Based Learning – III (Minor Project) | Project | 4 | Detailed Project Design, Advanced Implementation and Coding, System Integration and Testing, Result Analysis and Reporting, Presentation of Findings |
| 22CP702S | Internship/Industrial Training | Internship | 5 | Real-world Industry Experience, Practical Skill Application, Professional Work Environment Exposure, Problem-solving in Industrial Context, Technical Report Writing and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CP801P | Major Project | Project | 12 | Comprehensive System Design, Advanced Research and Development, Robust Implementation and Validation, Performance Evaluation and Optimization, Technical Report/Thesis Writing and Defense |
| Program Elective – III | Program Elective – III (e.g., Pattern Recognition, Quantum Computing, Speech Processing) | Elective | 3 | Deep dive into specialized AI/ML areas, Emerging technologies and paradigms, Advanced theoretical concepts, Practical applications and case studies, Future trends and challenges |
| Open Elective – II | Open Elective – II | Elective | 3 | Choice of interdisciplinary subjects, Non-technical skill enhancement, Exposure to diverse academic fields, Personal interest exploration, Holistic development |
