Comprehensive Curriculum Overview
The engineering program at M K University Patan is structured over eight semesters, with a balanced mix of core courses, departmental electives, science electives, and laboratory sessions designed to build both technical proficiency and critical thinking abilities.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Graphics | 2-0-2-3 | None |
| 1 | MAT101 | Mathematics I | 4-0-0-4 | None |
| 1 | PHY101 | Physics I | 3-0-0-3 | None |
| 1 | CHE101 | Chemistry I | 3-0-0-3 | None |
| 1 | BIO101 | Basic Biology | 2-0-0-2 | None |
| 1 | ENG102 | Introduction to Programming | 3-0-2-4 | None |
| 1 | CSE101 | Basic Electrical Circuits | 3-0-0-3 | None |
| 2 | MAT102 | Mathematics II | 4-0-0-4 | MAT101 |
| 2 | PHY102 | Physics II | 3-0-0-3 | PHY101 |
| 2 | CHE102 | Chemistry II | 3-0-0-3 | CHE101 |
| 2 | ENG201 | Data Structures and Algorithms | 3-0-2-4 | ENG102 |
| 2 | CSE201 | Digital Electronics | 3-0-2-4 | CSE101 |
| 2 | MECH201 | Thermodynamics | 3-0-0-3 | MAT101 |
| 2 | CIVIL201 | Fluid Mechanics | 3-0-0-3 | MAT101 |
| 2 | ENG202 | English Communication Skills | 2-0-0-2 | None |
| 3 | MAT201 | Mathematics III | 4-0-0-4 | MAT102 |
| 3 | CSE301 | Database Management Systems | 3-0-2-4 | ENG201 |
| 3 | MECH301 | Strength of Materials | 3-0-0-3 | MECH201 |
| 3 | CIVIL301 | Structural Analysis | 3-0-0-3 | CIVIL201 |
| 3 | ECE301 | Signals and Systems | 3-0-0-3 | MAT102 |
| 3 | ENG301 | Project Management | 2-0-0-2 | None |
| 4 | CSE401 | Operating Systems | 3-0-2-4 | CSE301 |
| 4 | MECH401 | Mechanics of Machines | 3-0-0-3 | MECH301 |
| 4 | CIVIL401 | Geotechnical Engineering | 3-0-0-3 | CIVIL301 |
| 4 | ECE401 | Control Systems | 3-0-0-3 | ECE301 |
| 4 | ENG401 | Industrial Ethics | 2-0-0-2 | None |
| 5 | CSE501 | Machine Learning | 3-0-2-4 | CSE401 |
| 5 | MECH501 | Heat Transfer | 3-0-0-3 | MECH401 |
| 5 | CIVIL501 | Transportation Engineering | 3-0-0-3 | CIVIL401 |
| 5 | ECE501 | Communication Systems | 3-0-0-3 | ECE401 |
| 5 | ENG501 | Leadership and Teamwork | 2-0-0-2 | None |
| 6 | CSE601 | Computer Vision | 3-0-2-4 | CSE501 |
| 6 | MECH601 | Advanced Dynamics | 3-0-0-3 | MECH501 |
| 6 | CIVIL601 | Environmental Engineering | 3-0-0-3 | CIVIL501 |
| 6 | ECE601 | Antenna Design | 3-0-2-4 | ECE501 |
| 6 | ENG601 | Entrepreneurship | 2-0-0-2 | None |
| 7 | CSE701 | Deep Learning | 3-0-2-4 | CSE601 |
| 7 | MECH701 | Robotics | 3-0-2-4 | MECH601 |
| 7 | CIVIL701 | Urban Planning | 3-0-0-3 | CIVIL601 |
| 7 | ECE701 | Embedded Systems | 3-0-2-4 | ECE601 |
| 7 | ENG701 | Research Methodology | 2-0-0-2 | None |
| 8 | CSE801 | Capstone Project | 4-0-0-4 | CSE701 |
| 8 | MECH801 | Final Year Thesis | 4-0-0-4 | MECH701 |
| 8 | CIVIL801 | Design Project | 4-0-0-4 | CIVIL701 |
| 8 | ECE801 | Final Year Research | 4-0-0-4 | ECE701 |
| 8 | ENG801 | Internship Report | 2-0-0-2 | None |
Each department offers a range of advanced elective courses tailored to specific specializations. These courses are designed to deepen students' understanding and prepare them for specialized roles in their chosen fields.
Advanced Departmental Electives
Machine Learning: This course introduces students to the fundamental concepts of machine learning, including supervised and unsupervised learning algorithms, neural networks, and deep learning architectures. Students learn to implement these techniques using Python libraries like Scikit-learn, TensorFlow, and PyTorch.
Computer Vision: Focused on image processing and recognition tasks, this course covers topics such as edge detection, feature extraction, object classification, and real-time video analysis. Practical sessions involve working with datasets from Kaggle and implementing models using OpenCV and YOLO frameworks.
Database Management Systems: This course delves into the design and implementation of relational databases, normalization techniques, transaction management, indexing strategies, and SQL query optimization. Students gain hands-on experience through lab exercises involving MySQL, PostgreSQL, and MongoDB.
Operating Systems: Covering both theoretical foundations and practical aspects, this course explores process management, memory allocation, file systems, security mechanisms, and virtualization technologies. Labs involve building simple OS kernels using C/C++ and understanding Linux internals.
Digital Electronics: Designed to give students a deep understanding of digital circuits and logic design principles, this course covers combinational and sequential logic circuits, flip-flops, counters, registers, and programmable logic devices (PLDs). Practical sessions include circuit simulation using Logisim and hardware prototyping.
Signals and Systems: This course explores the mathematical analysis of signals and systems, including Fourier transforms, Laplace transforms, Z-transforms, and convolution operations. Students apply these concepts to analyze communication systems and control systems.
Control Systems: Focused on modeling and analyzing feedback control systems, this course covers state-space representation, transfer functions, stability analysis, root locus techniques, and PID controller design. Practical labs involve using MATLAB/Simulink for simulation and real-time system testing.
Embedded Systems: This course provides an in-depth look at designing embedded applications using microcontrollers like Arduino and Raspberry Pi. Topics include real-time operating systems (RTOS), interrupt handling, sensor integration, and communication protocols such as I2C, SPI, UART, and CAN bus.
Communication Systems: Exploring the principles of modern communication techniques, this course covers analog and digital modulation schemes, noise analysis, channel coding, and wireless communication standards. Students conduct experiments with RF signal generators, oscilloscopes, and spectrum analyzers.
Artificial Intelligence: This advanced course introduces students to AI concepts such as expert systems, knowledge representation, planning algorithms, natural language processing, and robotics. Labs involve building intelligent agents using Python-based frameworks like NLTK and spaCy.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a core pedagogical strategy that enhances student engagement, develops problem-solving skills, and prepares graduates for industry-ready competencies. Projects are integrated throughout the curriculum to provide continuous exposure to real-world applications.
Mini-projects begin in the second year, allowing students to explore specific topics within their chosen field. These projects are typically completed over 3-4 weeks and involve small groups of 3-5 students. Students are assigned mentors from faculty members who guide them through the research process, data collection, analysis, and presentation.
The final-year capstone project is a major endeavor that spans the entire semester. Students select projects based on their interests or collaborate with industry partners to address practical challenges. These projects require extensive literature review, experimentation, documentation, and oral presentations. Evaluation criteria include innovation, technical depth, teamwork, clarity of communication, and impact assessment.
Faculty mentors play a crucial role in guiding students through each stage of the project lifecycle. They help students refine their ideas, suggest relevant resources, and ensure that the projects align with industry standards and academic rigor. Regular meetings and progress updates are mandatory to track development and address any issues promptly.
Projects often lead to publications, patents, or startup ventures, providing students with tangible achievements that enhance their resumes and open doors to further opportunities. The department also organizes annual project showcases where students present their work to faculty, industry representatives, and peers, fostering a culture of innovation and excellence.