Comprehensive AI Curriculum Overview
The AI curriculum at Get Group Of Institution Faculty Of Technology is meticulously designed to provide students with a robust foundation in both theoretical and practical aspects of artificial intelligence. The program spans four years, with each semester carefully structured to ensure progressive learning and skill development.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Computer Science | 3-1-0-4 | - |
| 1 | MA101 | Mathematics for AI | 3-1-0-4 | - |
| 1 | PH101 | Physics for Computing | 3-1-0-4 | - |
| 1 | CS102 | Programming Fundamentals | 3-0-2-4 | - |
| 1 | CH101 | Chemistry for Engineers | 3-1-0-4 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS102 |
| 2 | MA201 | Statistics and Probability | 3-1-0-4 | MA101 |
| 2 | CS202 | Database Systems | 3-1-0-4 | CS102 |
| 2 | CS203 | Software Engineering | 3-1-0-4 | CS102 |
| 2 | PH201 | Electromagnetic Fields and Waves | 3-1-0-4 | PH101 |
| 3 | CS301 | Foundations of Machine Learning | 3-1-0-4 | MA201, CS201 |
| 3 | CS302 | Data Mining | 3-1-0-4 | MA201, CS201 |
| 3 | CS303 | Artificial Intelligence Principles | 3-1-0-4 | CS201 |
| 3 | CS304 | Computer Vision Fundamentals | 3-1-0-4 | CS301 |
| 3 | CS305 | Natural Language Processing | 3-1-0-4 | CS301 |
| 4 | CS401 | Deep Learning | 3-1-0-4 | CS301, CS302 |
| 4 | CS402 | Reinforcement Learning | 3-1-0-4 | CS301 |
| 4 | CS403 | Neural Networks and Applications | 3-1-0-4 | CS401 |
| 4 | CS404 | AI Ethics and Governance | 3-1-0-4 | CS301 |
| 5 | CS501 | Advanced Machine Learning | 3-1-0-4 | CS401, CS402 |
| 5 | CS502 | Computational Linguistics | 3-1-0-4 | CS305 |
| 5 | CS503 | Robotics and Automation | 3-1-0-4 | CS303 |
| 5 | CS504 | AI for Healthcare | 3-1-0-4 | CS301, CS302 |
| 6 | CS601 | Research Methodology | 3-1-0-4 | CS501 |
| 6 | CS602 | Capstone Project I | 3-0-6-9 | CS501, CS503 |
| 7 | CS701 | Capstone Project II | 3-0-6-9 | CS602 |
| 7 | CS702 | Internship | 0-0-0-18 | - |
| 8 | CS801 | Final Thesis | 3-0-6-9 | CS701 |
| 8 | CS802 | Elective Course A | 3-1-0-4 | - |
| 8 | CS803 | Elective Course B | 3-1-0-4 | - |
Detailed Overview of Departmental Electives
The department offers a rich array of advanced elective courses that allow students to explore specialized areas within AI. Here are some key courses:
- Advanced Machine Learning (CS501): This course delves into modern machine learning techniques including ensemble methods, dimensionality reduction, and online learning algorithms. Students gain hands-on experience with real-world datasets and develop proficiency in advanced frameworks such as Scikit-learn, Keras, and PyTorch.
- Computational Linguistics (CS502): This course explores the intersection of linguistics and computer science, focusing on natural language processing technologies. Topics include syntax analysis, semantic interpretation, named entity recognition, and machine translation models.
- Robotics and Automation (CS503): Designed for students interested in physical AI systems, this course covers kinematics, control theory, sensor fusion, and autonomous navigation. Students work on projects involving robotic arms, drones, and mobile robots.
- AI for Healthcare (CS504): This interdisciplinary course examines how AI can transform healthcare delivery. It includes topics such as medical image analysis, predictive modeling for disease diagnosis, and personalized treatment plans using patient data.
Project-Based Learning Philosophy
The department strongly emphasizes project-based learning to ensure that students apply theoretical knowledge in practical settings. From the second year onwards, students engage in mini-projects designed to reinforce concepts learned in class and foster collaborative skills.
Mini-projects are typically completed in teams of 3-5 members and involve solving real-world problems using AI methodologies. Each project is supervised by a faculty mentor and evaluated based on technical execution, innovation, presentation quality, and teamwork.
The final-year thesis or capstone project is the culmination of the student's learning journey. Students select topics aligned with their interests and career goals, often inspired by ongoing research initiatives in the department. These projects are typically conducted under the guidance of a faculty advisor and may lead to publication opportunities or patent applications.
Capstone Project Structure
The capstone project spans two semesters—Semester 6 (Capstone I) and Semester 7 (Capstone II). During Capstone I, students identify potential research areas, conduct literature reviews, define objectives, and develop preliminary designs. This phase involves regular meetings with faculty advisors and submission of progress reports.
In Capstone II, students implement their proposed solutions, collect data, perform experiments, analyze results, and prepare a comprehensive report. The final deliverable includes a written thesis, oral presentation, and demonstration of the implemented system.
Students are encouraged to choose projects that align with current industry trends or emerging research areas. The department provides access to cutting-edge tools, datasets, and computational resources to support these endeavors.
Faculty Mentorship
Each student is assigned a faculty mentor during the early stages of their academic journey. Faculty mentors provide guidance on course selection, project planning, internship opportunities, and career development. Regular one-on-one sessions ensure personalized attention and support throughout the program.
The department maintains an open-door policy for faculty members, allowing students to seek advice and clarification anytime. Mentors also facilitate connections with alumni, industry professionals, and researchers working in related fields.