Course Listing Across All Semesters

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Prerequisites
1	CS101	Introduction to Computing	3-0-0-3	None
1	CS102	Programming in C	3-0-2-4	None
1	CS103	Mathematics for Computing	3-0-0-3	None
1	CS104	Computer Organization	3-0-0-3	None
1	CS105	English Communication	2-0-0-2	None
2	CS201	Data Structures and Algorithms	3-0-2-4	CS102
2	CS202	Digital Logic Design	3-0-0-3	CS104
2	CS203	Database Management Systems	3-0-2-4	CS102
2	CS204	Object-Oriented Programming with Java	3-0-2-4	CS102
2	CS205	Computer Networks	3-0-0-3	CS104
3	CS301	Operating Systems	3-0-2-4	CS201
3	CS302	Software Engineering	3-0-2-4	CS201
3	CS303	Compiler Design	3-0-0-3	CS201
3	CS304	Artificial Intelligence	3-0-2-4	CS201
3	CS305	Web Technologies	3-0-2-4	CS204
4	CS401	Machine Learning	3-0-2-4	CS304
4	CS402	Cybersecurity	3-0-2-4	CS205
4	CS403	Data Analytics	3-0-2-4	CS301
4	CS404	Internet of Things (IoT)	3-0-2-4	CS201
4	CS405	Human-Computer Interaction	3-0-0-3	CS204
5	CS501	Cloud Computing	3-0-2-4	CS401
5	CS502	Blockchain Technology	3-0-2-4	CS201
5	CS503	Advanced Software Engineering	3-0-2-4	CS302
5	CS504	Research Methodology	3-0-0-3	CS301
5	CS505	Embedded Systems	3-0-2-4	CS201
6	CS601	Capstone Project	3-0-6-9	CS501
6	CS602	Mini Project	3-0-4-7	CS501

Advanced Departmental Elective Courses

These advanced elective courses provide students with specialized knowledge and skills in niche areas of Computer Applications:

• Deep Learning with TensorFlow: This course introduces students to deep learning models using TensorFlow, focusing on convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer architectures. Students learn how to build and train neural networks for image recognition, natural language processing, and time series forecasting.
• Privacy-Preserving Machine Learning: As privacy becomes a critical concern in data science, this course explores techniques such as differential privacy, federated learning, and secure multi-party computation. Students gain hands-on experience implementing privacy-preserving algorithms using frameworks like PySyft and TensorFlow Privacy.
• DevOps Practices and Tools: Designed for students aiming to bridge the gap between development and operations, this course covers CI/CD pipelines, containerization with Docker, orchestration with Kubernetes, and automation tools like Jenkins and Ansible. Students work on real-world projects integrating these practices into software delivery processes.
• Quantum Computing Fundamentals: This course provides an introduction to quantum algorithms and their implementation using platforms like IBM Qiskit. Students learn about qubits, superposition, entanglement, and how quantum computers differ from classical ones in solving certain problems.
• Advanced Cryptography and Network Security: Building upon foundational knowledge of cryptography, this course explores advanced topics such as elliptic curve cryptography (ECC), hash-based signatures, and post-quantum cryptography. Students also study network security protocols like SSL/TLS and secure routing mechanisms.
• Computer Vision and Image Processing: This course covers techniques for analyzing visual data using deep learning models. Students learn about image segmentation, object detection, facial recognition, and augmented reality applications. Practical implementation involves working with datasets like ImageNet and COCO.
• Natural Language Processing (NLP) with Transformers: Focused on state-of-the-art NLP techniques, students study transformer architectures, BERT, GPT, and T5 models. The course includes building language understanding systems for tasks such as sentiment analysis, machine translation, and question answering.
• Reinforcement Learning Algorithms: This course introduces reinforcement learning concepts including Markov Decision Processes (MDPs), Q-learning, policy gradients, and actor-critic methods. Students apply these techniques to simulate decision-making in environments like Atari games and robotic control systems.
• Big Data Analytics with Apache Spark: Students learn to process large datasets using Apache Spark, focusing on data pipelines, streaming analytics, and machine learning workflows. Practical assignments involve real-world datasets from social media and financial sectors.
• Mobile Application Development (iOS/Android): This course covers the design and development of mobile applications for both iOS and Android platforms. Students learn about UI/UX principles, native APIs, cross-platform frameworks like React Native, and deployment strategies for app stores.

Project-Based Learning Philosophy

At Monad University Hapur, project-based learning is central to our educational philosophy. It emphasizes experiential learning where students actively engage in solving real-world problems through collaborative research and development efforts.

The program incorporates two major projects:

• Mini Projects (Semester 5): Students work in teams on short-term projects related to their chosen specialization. These projects are supervised by faculty mentors and evaluated based on technical depth, innovation, teamwork, and presentation quality.
• Final Year Thesis/Capstone Project (Semester 6): This is a comprehensive project undertaken over the final semester, involving extensive research, implementation, documentation, and defense. Students select projects based on their interests or industry requirements and are paired with faculty advisors who guide them throughout the process.

Students have multiple avenues to choose their projects:

• Faculty-led initiatives funded by grants or industry partnerships
• Industry-sponsored challenges posted through our placement cell
• Self-initiated ideas proposed by students with faculty approval

Evaluation criteria for these projects include technical feasibility, originality of approach, documentation standards, oral and written presentations, and peer feedback. This holistic assessment ensures that students develop both technical and communication skills essential for professional success.