Curriculum
The curriculum of the engineering program at I E C India Education Centre University Solan is designed to provide a comprehensive foundation in core engineering principles while allowing flexibility for specialization. The program spans eight semesters, with each semester comprising 15-20 credit hours distributed across core subjects, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | PHY101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CHE101 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | CP101 | Introduction to Programming | 2-0-2-3 | - |
| 1 | ENG102 | Engineering Drawing and Graphics | 2-0-2-3 | - |
| 2 | ENG103 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | MAT101 | Applied Mechanics | 3-1-0-4 | - |
| 2 | ECE101 | Basic Electrical Circuits | 3-1-0-4 | - |
| 2 | CP102 | Data Structures and Algorithms | 2-0-2-3 | CP101 |
| 2 | ENG104 | Introduction to Engineering Design | 2-0-2-3 | - |
| 3 | ENG105 | Engineering Mathematics III | 3-1-0-4 | ENG103 |
| 3 | MAT102 | Strength of Materials | 3-1-0-4 | MAT101 |
| 3 | ECE102 | Electronics Devices and Circuits | 3-1-0-4 | ECE101 |
| 3 | CP201 | Object-Oriented Programming | 2-0-2-3 | CP102 |
| 3 | ENG106 | Project Management and Ethics | 2-0-2-3 | - |
| 4 | ENG107 | Engineering Mathematics IV | 3-1-0-4 | ENG105 |
| 4 | MAT103 | Thermodynamics and Heat Transfer | 3-1-0-4 | MAT102 |
| 4 | ECE103 | Signals and Systems | 3-1-0-4 | ECE102 |
| 4 | CP202 | Databases and Information Systems | 2-0-2-3 | CP201 |
| 4 | ENG108 | Research Methodology and Innovation | 2-0-2-3 | - |
| 5 | ENG109 | Fluid Mechanics and Hydraulic Machines | 3-1-0-4 | MAT103 |
| 5 | MAT104 | Manufacturing Processes | 3-1-0-4 | - |
| 5 | ECE104 | Control Systems | 3-1-0-4 | ECE103 |
| 5 | CP301 | Software Engineering and Design Patterns | 2-0-2-3 | CP202 |
| 5 | ENG110 | Mini Project I | 0-0-6-3 | - |
| 6 | ENG111 | Design and Analysis of Algorithms | 3-1-0-4 | CP202 |
| 6 | MAT105 | Advanced Strength of Materials | 3-1-0-4 | MAT104 |
| 6 | ECE105 | Digital Signal Processing | 3-1-0-4 | ECE103 |
| 6 | CP302 | Machine Learning Fundamentals | 2-0-2-3 | CP202 |
| 6 | ENG112 | Mini Project II | 0-0-6-3 | ENG110 |
| 7 | ENG113 | Advanced Control Systems | 3-1-0-4 | ECE104 |
| 7 | MAT106 | Finite Element Methods | 3-1-0-4 | MAT105 |
| 7 | ECE106 | Communication Systems | 3-1-0-4 | ECE105 |
| 7 | CP401 | Artificial Intelligence and Neural Networks | 2-0-2-3 | CP302 |
| 7 | ENG114 | Capstone Project I | 0-0-12-6 | ENG112 |
| 8 | ENG115 | Advanced Signal Processing Techniques | 3-1-0-4 | ECE106 |
| 8 | MAT107 | Sustainable Engineering Practices | 3-1-0-4 | - |
| 8 | ECE107 | Cybersecurity Fundamentals | 3-1-0-4 | - |
| 8 | CP402 | Cloud Computing and DevOps | 2-0-2-3 | CP302 |
| 8 | ENG116 | Capstone Project II | 0-0-12-6 | ENG114 |
Advanced departmental elective courses are offered to deepen student expertise in specialized areas. For instance, 'Deep Learning' explores neural network architectures and their applications in computer vision and natural language processing. The course is taught by Dr. Arvind Sharma, who has published extensively in top-tier conferences like NeurIPS and ICML.
'Natural Language Processing' focuses on building systems that understand human languages. It covers topics such as sentiment analysis, machine translation, and dialogue systems. The course is led by Dr. Priya Gupta, whose research has been cited over 1000 times in leading journals.
'Computer Vision' introduces students to image processing techniques and object detection algorithms. Students gain hands-on experience with OpenCV and TensorFlow frameworks. Professor Rajesh Kumar, a recipient of the IEEE Fellow Award, leads this course.
'Reinforcement Learning' teaches students how agents learn through interaction with environments. The curriculum includes Markov Decision Processes, Q-learning, and policy gradients. Dr. Nandita Mehta guides this course, having authored several papers in top-tier AI conferences.
'Network Security' covers intrusion detection, firewall configurations, and secure network design. It is taught by Dr. Suresh Reddy, whose work has been instrumental in developing industry-standard cybersecurity frameworks.
'Cryptography and Network Security' delves into encryption algorithms, digital signatures, and public key infrastructure. The course is led by Dr. Nandita Mehta, who has contributed to several cryptographic standards adopted globally.
'Finite Element Analysis' teaches numerical methods for solving engineering problems using computational software. It includes structural analysis, heat transfer, and fluid dynamics simulations. Professor Suresh Reddy provides instruction in this course, which involves extensive lab work using ANSYS and ABAQUS.
'Digital Signal Processing' explores filtering techniques, spectral analysis, and system identification. Students learn to implement DSP algorithms using MATLAB and Python. The course is led by Dr. Arvind Sharma, who has worked with major telecommunications companies on signal processing applications.
'Machine Learning in Healthcare' applies ML models to medical datasets for disease prediction and diagnosis. It covers supervised learning, unsupervised clustering, and deep learning for biomedical imaging. Dr. Priya Gupta leads this course, focusing on real-world case studies from hospitals and research institutions.
'Software Architecture and Design Patterns' focuses on scalable software systems and modular design principles. Students learn about microservices, cloud-native applications, and architectural patterns like MVC and MVVM. The course is led by Dr. Rajesh Kumar, who has designed enterprise-level software solutions for Fortune 500 companies.
'Advanced Computer Vision' builds upon foundational concepts in computer vision to explore advanced topics such as pose estimation, object tracking, and scene understanding. It includes hands-on projects with real-world datasets. Professor Nandita Mehta supervises this course, which involves working with industry partners on practical applications.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means to enhance critical thinking and problem-solving abilities. Projects are integrated throughout the curriculum to provide students with continuous exposure to real-world challenges. The mandatory mini-projects in semesters 5 and 7 help students apply theoretical concepts practically, while the final-year capstone projects serve as a culmination of their academic journey.
Mini-projects typically span 3-4 months and involve teams of 3-5 students working under faculty supervision. Students are required to submit detailed project reports, present findings to peers, and defend their approaches in front of industry mentors. Evaluation criteria include technical depth, creativity, presentation quality, and teamwork.
The final-year thesis or capstone project is a significant undertaking that allows students to explore a topic of personal interest or relevance to current industry trends. Projects are selected based on student preferences, faculty expertise, and alignment with departmental research initiatives. Students work closely with assigned mentors throughout the duration of their projects.
Project selection involves an application process where students propose ideas based on available resources and faculty availability. The department maintains a database of approved project topics that align with ongoing research or industry needs. This ensures that students engage in meaningful, impactful work that contributes to both personal development and professional growth.