Course List Across 8 Semesters
Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
---|---|---|---|---|
1 | ENG101 | English for Engineering Communication | 3-0-0-3 | - |
1 | MAT101 | Mathematics I | 4-0-0-4 | - |
1 | PHY101 | Physics for Engineers | 3-0-0-3 | - |
1 | CHM101 | Chemistry for Engineering Students | 3-0-0-3 | - |
1 | CSE101 | Introduction to Programming | 2-0-2-4 | - |
1 | ESC101 | Engineering Graphics and Design | 2-0-2-4 | - |
1 | ELN101 | Basic Electrical Engineering | 3-0-0-3 | - |
1 | CSE102 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
1 | MAT102 | Mathematics II | 4-0-0-4 | MAT101 |
1 | PHY102 | Optics, Waves and Modern Physics | 3-0-0-3 | PHY101 |
2 | CSE201 | Object Oriented Programming using C++ | 3-0-0-3 | CSE102 |
2 | MAT201 | Mathematics III | 4-0-0-4 | MAT102 |
2 | CHM201 | Physical Chemistry | 3-0-0-3 | CHM101 |
2 | PHY201 | Thermodynamics and Statistical Mechanics | 3-0-0-3 | PHY102 |
2 | CSE202 | Database Management Systems | 3-0-0-3 | CSE102 |
2 | ELN201 | Electromagnetic Fields and Circuits | 3-0-0-3 | ELN101 |
2 | ESC201 | Mechanics of Materials | 3-0-0-3 | - |
3 | CSE301 | Operating Systems | 3-0-0-3 | CSE202 |
3 | MAT301 | Mathematics IV | 4-0-0-4 | MAT201 |
3 | CHM301 | Inorganic Chemistry | 3-0-0-3 | CHM201 |
3 | PHY301 | Quantum Mechanics and Nuclear Physics | 3-0-0-3 | PHY201 |
3 | CSE302 | Computer Networks | 3-0-0-3 | CSE202 |
3 | ELN301 | Signal and Systems | 3-0-0-3 | ELN201 |
3 | ESC301 | Strength of Materials | 3-0-0-3 | ESC201 |
4 | CSE401 | Software Engineering | 3-0-0-3 | CSE301 |
4 | MAT401 | Mathematics V | 4-0-0-4 | MAT301 |
4 | CHM401 | Organic Chemistry | 3-0-0-3 | CHM301 |
4 | PHY401 | Electromagnetic Waves and Optics | 3-0-0-3 | PHY301 |
4 | CSE402 | Machine Learning | 3-0-0-3 | CSE302 |
4 | ELN401 | Control Systems | 3-0-0-3 | ELN301 |
4 | ESC401 | Design of Machine Elements | 3-0-0-3 | ESC301 |
5 | CSE501 | Artificial Intelligence | 3-0-0-3 | CSE402 |
5 | MAT501 | Advanced Mathematics | 4-0-0-4 | MAT401 |
5 | CHM501 | Physical Organic Chemistry | 3-0-0-3 | CHM401 |
5 | PHY501 | Atomic and Molecular Physics | 3-0-0-3 | PHY401 |
5 | CSE502 | Big Data Analytics | 3-0-0-3 | CSE402 |
5 | ELN501 | Digital Signal Processing | 3-0-0-3 | ELN401 |
5 | ESC501 | Thermodynamics and Heat Transfer | 3-0-0-3 | ESC401 |
6 | CSE601 | Deep Learning | 3-0-0-3 | CSE501 |
6 | MAT601 | Probability and Statistics | 4-0-0-4 | MAT501 |
6 | CHM601 | Chemical Kinetics | 3-0-0-3 | CHM501 |
6 | PHY601 | Nuclear and Particle Physics | 3-0-0-3 | PHY501 |
6 | CSE602 | Cloud Computing | 3-0-0-3 | CSE502 |
6 | ELN601 | Microprocessors and Microcontrollers | 3-0-0-3 | ELN501 |
6 | ESC601 | Manufacturing Processes | 3-0-0-3 | ESC501 |
7 | CSE701 | Computer Vision | 3-0-0-3 | CSE601 |
7 | MAT701 | Applied Mathematics | 4-0-0-4 | MAT601 |
7 | CHM701 | Advanced Organic Chemistry | 3-0-0-3 | CHM601 |
7 | PHY701 | Quantum Field Theory | 3-0-0-3 | PHY601 |
7 | CSE702 | Internet of Things | 3-0-0-3 | CSE602 |
7 | ELN701 | Antennas and Wave Propagation | 3-0-0-3 | ELN601 |
7 | ESC701 | Advanced Materials | 3-0-0-3 | ESC601 |
8 | CSE801 | Capstone Project | 4-0-0-4 | CSE702 |
8 | MAT801 | Mathematical Modeling | 4-0-0-4 | MAT701 |
8 | CHM801 | Chemical Engineering Principles | 3-0-0-3 | CHM701 |
8 | PHY801 | Advanced Physics | 3-0-0-3 | PHY701 |
8 | CSE802 | Research Methodology | 3-0-0-3 | CSE801 |
8 | ELN801 | Embedded Systems | 3-0-0-3 | ELN701 |
8 | ESC801 | Project Management | 3-0-0-3 | ESC701 |
Advanced Departmental Elective Courses
Advanced departmental electives at Abes Engineering College are designed to provide students with specialized knowledge and skills in their chosen fields. These courses go beyond the core curriculum, offering deeper insights into emerging technologies and applications.
One such course is 'Deep Learning,' which explores advanced neural network architectures, including convolutional networks for image recognition and recurrent networks for sequence modeling. Students learn to implement models using frameworks like TensorFlow and PyTorch, applying them to real-world datasets.
'Internet of Things (IoT)' delves into the integration of sensors, devices, and cloud platforms to create smart environments. The course covers protocols like MQTT, CoAP, and HTTP, along with practical implementations in home automation, smart agriculture, and industrial monitoring systems.
'Computer Vision' focuses on algorithms that enable computers to interpret and understand visual information from the world. Topics include image segmentation, object detection, facial recognition, and 3D reconstruction techniques using OpenCV and other libraries.
'Big Data Analytics' introduces students to tools like Hadoop, Spark, and Kafka for processing large datasets. The course emphasizes data mining, machine learning algorithms, and statistical analysis to extract meaningful insights from complex data structures.
'Cloud Computing' covers virtualization technologies, cloud service models (IaaS, PaaS, SaaS), and deployment strategies. Students gain hands-on experience with platforms like AWS, Azure, and Google Cloud, building scalable applications and services.
'Machine Learning' explores supervised and unsupervised learning techniques, including decision trees, random forests, clustering algorithms, and neural networks. The course includes practical projects where students develop predictive models for various domains such as healthcare, finance, and marketing.
'Software Engineering' teaches the principles of software design, development, testing, and maintenance. Students work in teams to build full-stack applications using agile methodologies, version control systems like Git, and continuous integration tools.
'Cybersecurity' focuses on protecting digital assets from threats such as malware, phishing, and data breaches. The course covers encryption methods, network security protocols, incident response procedures, and ethical hacking practices.
'Data Structures and Algorithms' is a foundational course that enhances problem-solving abilities through algorithmic thinking. Students study sorting, searching, graph traversal, dynamic programming, and complexity analysis techniques.
'Database Management Systems' introduces students to relational and non-relational databases, SQL queries, normalization, indexing, and transaction management. The course includes practical sessions on designing and optimizing database schemas.
'Computer Networks' covers network architecture, protocols, routing algorithms, and network security. Students learn about LAN, WAN, wireless networks, and internet services, with lab sessions involving packet analysis and simulation tools.
'Operating Systems' explores system design principles, process management, memory allocation, file systems, and concurrency control mechanisms. The course includes practical assignments on OS concepts using Linux environments.
Project-Based Learning Philosophy
At Abes Engineering College, project-based learning is central to the B.Tech experience. This approach integrates theoretical knowledge with practical application, fostering innovation and critical thinking among students.
Mini-projects begin in the second year, allowing students to explore specific topics within their field of study. These projects are typically completed in groups, promoting teamwork and communication skills. Each project is guided by a faculty mentor who provides technical support and feedback throughout the process.
The final-year thesis or capstone project represents the culmination of the student's academic journey. Students select a topic based on their interests and career goals, often collaborating with industry partners or research organizations. The project involves extensive literature review, experimental design, data collection, analysis, and presentation.
Evaluation criteria for projects include technical depth, creativity, adherence to timelines, quality of deliverables, and oral defense performance. Students are encouraged to present their work at conferences, publish papers, or apply for patents.