Course Overview and Credit Structure
The Artificial Intelligence program at SCHOOL OF COMPUTER APPLICATION SRI SATYA SAI UNIVERSITY OF TECHNOLOGY AND MEDICAL SCIENCES SSSUTMS is structured over eight semesters, with a total of 160 credits required for graduation. The curriculum balances theoretical knowledge with practical implementation, emphasizing problem-solving, critical thinking, and innovation.
First Year
| Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS101 | Introduction to Programming | 3-1-0-4 | None |
| MA101 | Engineering Mathematics I | 3-0-0-3 | None |
| PH101 | Physics for Computer Science | 3-0-0-3 | None |
| CH101 | Chemistry for Engineers | 3-0-0-3 | None |
| EE101 | Basic Electrical Engineering | 3-0-0-3 | None |
| HS101 | English Communication Skills | 2-0-0-2 | None |
| GE101 | General Education | 2-0-0-2 | None |
| CE101 | Computer Engineering Fundamentals | 2-0-0-2 | None |
Second Year
| Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS201 | Data Structures and Algorithms | 3-1-0-4 | CS101 |
| MA201 | Engineering Mathematics II | 3-0-0-3 | MA101 |
| CS202 | Database Management Systems | 3-0-0-3 | CS101 |
| CS203 | Object-Oriented Programming with Java | 3-1-0-4 | CS101 |
| PH201 | Modern Physics | 3-0-0-3 | PH101 |
| EE201 | Electrical Circuits and Networks | 3-0-0-3 | EE101 |
| HS201 | Professional Communication | 2-0-0-2 | HS101 |
| GE201 | General Education II | 2-0-0-2 | GE101 |
Third Year
| Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS301 | Artificial Intelligence Fundamentals | 3-1-0-4 | CS201, MA201 |
| CS302 | Machine Learning Basics | 3-1-0-4 | CS201, MA201 |
| CS303 | Computer Vision and Image Processing | 3-1-0-4 | CS201, CS202 |
| CS304 | Natural Language Processing | 3-1-0-4 | CS301 |
| CS305 | Robotics and Control Systems | 3-1-0-4 | EE201, CS201 |
| CS306 | Neural Networks | 3-1-0-4 | CS301, MA201 |
| CS307 | Deep Learning | 3-1-0-4 | CS301, CS306 |
| CS308 | Human-AI Interaction | 2-1-0-3 | CS301 |
Fourth Year
| Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|
| CS401 | Advanced Machine Learning | 3-1-0-4 | CS302, CS306 |
| CS402 | AI in Healthcare | 3-1-0-4 | CS301, CS303 |
| CS403 | Autonomous Systems | 3-1-0-4 | CS305 |
| CS404 | AI Ethics and Governance | 2-1-0-3 | CS301, CS302 |
| CS405 | Capstone Project I | 4-0-0-4 | CS301, CS302 |
| CS406 | Capstone Project II | 4-0-0-4 | CS405 |
| CS407 | Research Methodology | 2-0-0-2 | None |
| CS408 | Entrepreneurship in AI | 2-0-0-2 | None |
Departmental Electives (Third and Fourth Years)
- Advanced Statistical Learning: Covers Bayesian inference, probabilistic graphical models, and advanced regression techniques.
- Big Data Analytics: Focuses on scalable data processing using Hadoop, Spark, and streaming analytics tools.
- Optimization Techniques for AI: Applies mathematical optimization methods to machine learning problems.
- Reinforcement Learning: Explores algorithms like Q-learning, policy gradients, and actor-critic methods.
- Speech Recognition: Covers signal processing techniques and deep learning models for speech-to-text conversion.
- Cognitive Computing: Investigates human-like reasoning systems and knowledge representation.
- Computer Vision in Robotics: Combines computer vision with robotic navigation and control.
- Neural Architecture Search: Automates the design of neural networks using reinforcement learning and evolutionary algorithms.
- Explainable AI: Develops methods to interpret machine learning decisions and enhance transparency.
- Quantum Machine Learning: Introduces quantum computing concepts for solving ML problems.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a core component of the curriculum. From first year, students engage in mini-projects that build foundational skills and encourage collaboration. These projects are designed to mirror real-world challenges, allowing students to apply theoretical knowledge in practical settings.
Mini-projects begin with guided tutorials in early semesters and evolve into independent research tasks. Students choose their own project topics based on faculty mentorship and personal interest. The selection process involves group discussions, proposal presentations, and peer reviews.
The final-year thesis or capstone project is the culmination of all learned knowledge. It requires students to work closely with a faculty advisor, develop a comprehensive research question, conduct literature review, implement solutions, and present findings in both written and oral formats. Projects are evaluated using rubrics that assess technical depth, creativity, clarity of communication, and impact.