Comprehensive Course Structure
The AI program at Aditya University Kakinada is structured over 8 semesters, with a balanced mix of core courses, departmental electives, science electives, and lab sessions. Below is a detailed table outlining each course, its code, credit structure (L-T-P-C), and pre-requisites:
| SEMESTER | COURSE CODE | COURSE TITLE | L-T-P-C | PREREQUISITES |
|---|---|---|---|---|
| I | CS101 | Introduction to Programming | 3-0-0-3 | None |
| I | MATH101 | Calculus and Analytical Geometry | 4-0-0-4 | None |
| I | MATH102 | Linear Algebra | 3-0-0-3 | MATH101 |
| I | PHY101 | Physics for Computer Science | 3-0-0-3 | None |
| I | CS102 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| I | CS103 | Discrete Mathematics | 3-0-0-3 | MATH101 |
| II | CS201 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| II | MATH201 | Probability and Statistics | 3-0-0-3 | MATH102 |
| II | MATH202 | Differential Equations | 3-0-0-3 | MATH101 |
| II | CS202 | Database Systems | 3-0-0-3 | CS101 |
| II | CS203 | Computer Organization and Architecture | 3-0-0-3 | CS101 |
| III | CS301 | Machine Learning Fundamentals | 3-0-0-3 | MATH201, CS202 |
| III | CS302 | Artificial Intelligence Concepts | 3-0-0-3 | CS201 |
| III | CS303 | Linear Algebra and Optimization | 3-0-0-3 | MATH102 |
| III | CS304 | Probability Theory | 3-0-0-3 | MATH201 |
| IV | CS401 | Neural Networks and Deep Learning | 3-0-0-3 | CS301, CS303 |
| IV | CS402 | Natural Language Processing | 3-0-0-3 | CS301, MATH201 |
| IV | CS403 | Computer Vision | 3-0-0-3 | CS301, CS303 |
| IV | CS404 | Reinforcement Learning | 3-0-0-3 | CS301, MATH201 |
| V | CS501 | Advanced Machine Learning Techniques | 3-0-0-3 | CS401 |
| V | CS502 | AI Ethics and Governance | 3-0-0-3 | CS302 |
| V | CS503 | Special Topics in AI | 3-0-0-3 | CS401 |
| V | CS504 | AI for Healthcare Applications | 3-0-0-3 | CS401, CS402 |
| VI | CS601 | Capstone Project I | 0-0-6-3 | CS501 |
| VI | CS602 | Industry Internship | 0-0-0-6 | CS501 |
| VII | CS701 | Capstone Project II | 0-0-6-3 | CS601 |
| VIII | CS801 | Final Year Thesis | 0-0-6-6 | CS701 |
Besides the core courses, students are required to take departmental electives based on their specialization. The following are advanced elective courses offered in the program:
Advanced Departmental Elective Courses
CS501 – Advanced Machine Learning Techniques: This course delves into advanced topics in machine learning such as ensemble methods, Bayesian modeling, and semi-supervised learning. Students will implement these techniques using Python libraries like Scikit-learn, TensorFlow, and PyTorch.
CS502 – AI Ethics and Governance: The course explores ethical frameworks for AI development, privacy concerns, and regulatory compliance. It includes case studies on responsible AI deployment in healthcare, finance, and autonomous systems.
CS503 – Special Topics in AI: This elective covers emerging areas such as explainable AI (XAI), adversarial machine learning, and human-AI interaction. Students engage with current research papers and participate in weekly discussion sessions.
CS504 – AI for Healthcare Applications: The course focuses on applying AI techniques to medical imaging, drug discovery, genomics, and personalized treatment plans. It includes hands-on projects with real-world datasets from hospitals and pharmaceutical companies.
CS601 – Capstone Project I: This is the first phase of the capstone project where students work in teams under faculty supervision to develop a prototype AI system. The project must align with one of the specializations chosen by the student.
CS602 – Industry Internship: Students are placed in companies for 6 months to gain practical experience. During this period, they contribute to real projects and receive mentorship from industry professionals.
CS701 – Capstone Project II: In the second phase of the capstone project, students refine their prototype based on feedback from mentors and stakeholders. The final deliverable includes a detailed report, presentation, and code repository.
CS801 – Final Year Thesis: The thesis is an original research contribution by the student. It involves conducting independent research under the guidance of a faculty advisor and presenting findings in a formal paper and oral defense.
Project-Based Learning Philosophy
The AI program at Aditya University places great emphasis on project-based learning to ensure students develop practical skills that are directly applicable in industry. This approach integrates theoretical knowledge with hands-on experience, allowing students to solve real-world problems using AI techniques.
The structure of the project-based learning includes:
- Mini Projects (Year 2): Students work individually or in small groups on short-term projects related to machine learning and data analysis. These projects are evaluated based on code quality, documentation, and presentation skills.
- Capstone Projects (Years 3-4): The capstone project is a major undertaking where students work on long-term research or development tasks. These projects often involve collaboration with industry partners and require advanced technical skills.
Evaluation criteria for projects include:
- Technical Execution
- Innovation and Creativity
- Team Collaboration
- Documentation Quality
- Presentation Skills
- Impact on Society or Industry
Students are encouraged to select their projects based on personal interest and career goals. Faculty members guide students in choosing suitable topics and provide support throughout the development process.