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Fees
₹6,00,000
Placement
94.0%
Avg Package
₹7,50,000
Highest Package
₹18,00,000
Fees
₹6,00,000
Placement
94.0%
Avg Package
₹7,50,000
Highest Package
₹18,00,000
Seats
300
Students
1,200
Seats
300
Students
1,200
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1st Semester | ENG101 | English for Engineers | 3-0-0-3 | - |
| MAT101 | Calculus and Differential Equations | 4-0-0-4 | - | |
| PHY101 | Physics for Engineers | 3-0-0-3 | - | |
| CHM101 | Chemistry for Engineers | 3-0-0-3 | - | |
| CSE101 | Introduction to Programming | 3-0-0-3 | - | |
| MAT102 | Linear Algebra and Probability | 3-0-0-3 | MAT101 | |
| CSE102 | Computer Organization & Architecture | 3-0-0-3 | - | |
| Labs | Physics Lab, Chemistry Lab | - | - | |
| 2nd Semester | MAT103 | Statistics and Numerical Methods | 3-0-0-3 | MAT101 |
| PHY102 | Electromagnetic Fields and Waves | 3-0-0-3 | PHY101 | |
| CSE103 | Data Structures and Algorithms | 3-0-0-3 | CSE101 | |
| ECE101 | Basic Electronics | 3-0-0-3 | - | |
| MAT104 | Differential Equations | 3-0-0-3 | MAT102 | |
| CSE104 | Object-Oriented Programming | 3-0-0-3 | CSE101 | |
| ECE102 | Digital Logic Design | 3-0-0-3 | ECE101 | |
| Labs | Mathematics Lab, Programming Lab | - | - | |
| 3rd Semester | MAT201 | Complex Variables and Transforms | 3-0-0-3 | MAT104 |
| CSE201 | Database Management Systems | 3-0-0-3 | CSE103 | |
| ECE201 | Signals and Systems | 3-0-0-3 | ECE102 | |
| CSE202 | Operating Systems | 3-0-0-3 | CSE104 | |
| MAT202 | Applied Mathematics for Engineers | 3-0-0-3 | MAT103 | |
| CSE203 | Software Engineering | 3-0-0-3 | CSE201 | |
| ECE202 | Electromagnetic Waves and Antennas | 3-0-0-3 | ECE201 | |
| Labs | Database Lab, Operating Systems Lab | - | - | |
| 4th Semester | MAT203 | Partial Differential Equations | 3-0-0-3 | MAT201 |
| CSE301 | Computer Networks | 3-0-0-3 | CSE202 | |
| ECE301 | Control Systems | 3-0-0-3 | ECE202 | |
| CSE302 | Artificial Intelligence and Machine Learning | 3-0-0-3 | CSE201 | |
| MAT204 | Probability and Statistics | 3-0-0-3 | MAT202 | |
| CSE303 | Web Technologies | 3-0-0-3 | CSE203 | |
| ECE302 | Microprocessors and Microcontrollers | 3-0-0-3 | ECE201 | |
| Labs | Networks Lab, AI/ML Lab | - | - | |
| 5th Semester | CSE401 | Machine Learning | 3-0-0-3 | CSE302 |
| ECE401 | VLSI Design | 3-0-0-3 | ECE302 | |
| CSE402 | Data Mining and Analytics | 3-0-0-3 | CSE301 | |
| CSE403 | Cloud Computing | 3-0-0-3 | CSE301 | |
| MAT301 | Optimization Techniques | 3-0-0-3 | MAT203 | |
| CSE404 | Big Data Analytics | 3-0-0-3 | CSE402 | |
| ECE402 | Embedded Systems | 3-0-0-3 | ECE301 | |
| Labs | Big Data Lab, Embedded Systems Lab | - | - | |
| 6th Semester | CSE501 | Internet of Things (IoT) | 3-0-0-3 | CSE403 |
| ECE501 | Wireless Communication | 3-0-0-3 | ECE401 | |
| CSE502 | Security in Computing | 3-0-0-3 | CSE302 | |
| CSE503 | Recommender Systems | 3-0-0-3 | CSE401 | |
| MAT302 | Discrete Mathematics | 3-0-0-3 | MAT201 | |
| CSE504 | Natural Language Processing | 3-0-0-3 | CSE401 | |
| ECE502 | Power Electronics | 3-0-0-3 | ECE402 | |
| Labs | IoT Lab, NLP Lab | - | - | |
| 7th Semester | CSE601 | Deep Learning | 3-0-0-3 | CSE503 |
| ECE601 | RF and Microwave Engineering | 3-0-0-3 | ECE501 | |
| CSE602 | Blockchain Technologies | 3-0-0-3 | CSE502 | |
| CSE603 | Computer Vision | 3-0-0-3 | CSE501 | |
| MAT303 | Mathematical Modeling | 3-0-0-3 | MAT301 | |
| CSE604 | Quantum Computing | 3-0-0-3 | CSE503 | |
| ECE602 | Advanced Embedded Systems | 3-0-0-3 | ECE502 | |
| Labs | Deep Learning Lab, Quantum Computing Lab | - | - | |
| 8th Semester | CSE701 | Capstone Project | 3-0-0-3 | All previous semesters |
| ECE701 | Final Year Thesis | 3-0-0-3 | All previous semesters | |
| CSE702 | Internship | 3-0-0-3 | All previous semesters | |
| CSE703 | Industry Collaboration Project | 3-0-0-3 | All previous semesters | |
| MAT304 | Advanced Topics in Engineering Mathematics | 3-0-0-3 | MAT302 | |
| CSE704 | Research Methodology | 3-0-0-3 | All previous semesters | |
| ECE702 | Advanced Power Systems | 3-0-0-3 | ECE601 | |
| Labs | Final Year Project Lab, Thesis Lab | - | - |
The department offers a range of advanced elective courses designed to provide students with specialized knowledge and skills in their chosen fields. These courses are taught by leading faculty members who are experts in their respective domains.
This course delves into the principles and applications of machine learning algorithms, covering supervised and unsupervised learning techniques, neural networks, deep learning frameworks, and reinforcement learning. Students learn to implement these concepts using Python and TensorFlow, gaining hands-on experience with real-world datasets. The course includes projects involving image recognition, natural language processing, and recommendation systems.
This elective explores the techniques used to extract meaningful patterns from large datasets. Topics include data preprocessing, clustering, classification, association rule mining, and anomaly detection. Students use tools like Weka, RapidMiner, and Python libraries such as scikit-learn to analyze real-world datasets in domains such as healthcare, finance, and marketing.
This course provides a comprehensive overview of cloud computing technologies, including virtualization, distributed systems, scalability, and security. Students learn about major platforms like AWS, Azure, and Google Cloud, gaining practical experience through lab exercises and capstone projects involving deployment of web applications in the cloud.
The course introduces students to the fundamentals of IoT systems, covering sensor networks, embedded systems, communication protocols, and data processing. Through lab sessions, students build IoT applications using Raspberry Pi, Arduino, and microcontrollers, focusing on smart home automation, environmental monitoring, and industrial IoT solutions.
This course covers cybersecurity principles, including network security, cryptography, access control, and malware analysis. Students study ethical hacking techniques, penetration testing, and secure software development practices. The curriculum includes hands-on labs using tools like Wireshark, Metasploit, and Kali Linux to simulate real-world attacks and defenses.
This course focuses on designing and implementing recommender systems that personalize user experiences based on preferences and behavior patterns. Students explore collaborative filtering, content-based filtering, hybrid methods, and deep learning approaches for recommendation engines. The course includes projects involving Netflix-style movie recommendations and Amazon-style product suggestions.
This elective covers the techniques used to process and understand human language using computational methods. Topics include text preprocessing, sentiment analysis, named entity recognition, machine translation, and question answering systems. Students work with libraries like NLTK, spaCy, and Hugging Face Transformers to develop NLP applications for various domains.
This advanced course explores deep neural networks, convolutional networks, recurrent networks, and transformers. Students learn to design and train complex models using frameworks like TensorFlow and PyTorch, applying them to image classification, speech recognition, and text generation tasks. The course includes hands-on labs involving computer vision projects and language modeling.
This course introduces students to blockchain architecture, consensus mechanisms, smart contracts, and decentralized applications. Through practical sessions, students develop blockchain-based solutions using platforms like Ethereum and Hyperledger Fabric, exploring applications in supply chain management, digital identity verification, and financial services.
This course covers image processing techniques, object detection, segmentation, and recognition algorithms. Students learn to implement computer vision models using OpenCV, TensorFlow, and PyTorch, applying them to facial recognition, autonomous vehicles, medical imaging, and robotics.
This emerging course introduces quantum mechanics principles and quantum algorithms, focusing on quantum computing frameworks like Qiskit and Cirq. Students explore applications in cryptography, optimization, and simulation of quantum systems, developing skills for the future of computing.
The department emphasizes project-based learning as a core component of its educational approach. This philosophy is rooted in the belief that real-world problem-solving skills are best developed through hands-on experience and collaborative teamwork. Students engage in multiple projects throughout their academic journey, from early-stage mini-projects to final-year capstone endeavors.
Mini-projects are introduced in the second and third years of study. These short-term initiatives allow students to explore specific areas of interest under faculty guidance. Projects typically last 4–6 weeks and involve small teams working on defined problems with clear deliverables. Examples include developing a simple mobile app, analyzing a dataset using statistical methods, or designing an algorithm for a particular task.
The final-year capstone project represents the culmination of a student's engineering education. Students select a topic aligned with their interests and career aspirations, working closely with a faculty advisor to develop a comprehensive solution. The project involves extensive research, experimentation, documentation, and presentation before a panel of experts. Successful projects often lead to publications, patents, or commercial applications.
Students begin the project selection process in their third year by attending project showcase events, where faculty members present current research opportunities and industry collaborations. Students are encouraged to propose ideas, seek feedback, and refine their concepts before finalizing a topic. The selection is based on academic performance, interest alignment, and resource availability.
Projects are evaluated based on several criteria including technical merit, innovation, documentation quality, teamwork, and presentation skills. Regular milestones and peer reviews ensure continuous improvement throughout the project lifecycle. Final evaluations are conducted by a panel of faculty members and industry experts to assess the impact and relevance of the work.
