UG – Artificial Intelligence
Innovating the ecosystem with automated systems, cognitive machines, and smart engineering tech.[cite: 5]
What Students Learn
A specialization track emphasizing algorithmic reasoning, deep synthesis paradigms, knowledge matrices, and industrial automation execution blueprints.[cite: 5]
What Do Students Learn?
- Fundamentals of AI & Expert Systems[cite: 5]
- Machine Learning & Data Modeling[cite: 5]
- Neural Networks & Deep Learning[cite: 5]
- Natural Language Processing[cite: 5]
- Computer Vision & Image Processing[cite: 5]
- Python and R Programming Foundations[cite: 5]
- Big Data & Knowledge Representation[cite: 5]
- AI Autonomous Project Development[cite: 5]
Academic Regulations
Active Framework
Download R23 Regulation
R23 Regulations
Comprehensive course layout structural matrix, credit allocation limits, assessment frameworks, and dynamic engineering evaluation metrics for ongoing batches.
Upcoming Framework
Download R26 Regulation
R26 Regulations
Next-generation curriculum optimization structural maps introducing advanced laboratory arrays, industry-aligned tech matrices, and updated parallel research loops.
Skills Developed
- AI-based Complex Problem Solving[cite: 5]
- Data Analytics & Multi-array Visualization[cite: 5]
- Advanced Algorithmic Thinking Patterns[cite: 5]
- Research Methodology Loops[cite: 5]
- Innovation & Structural Design Thinking[cite: 5]
- Real-Time Edge AI Applications[cite: 5]
Career Tracks
AI Researcher Scientist[cite: 5]
Machine Learning Infrastructure Engineer[cite: 5]
Data Scientist Lead[cite: 5]
AI Product Matrix Designer[cite: 5]
Healthcare/Finance AI Developer[cite: 5]
R&D Robotics Automation Specialist[cite: 5]
Course Matrix
Duration
4 Years undergraduate track[cite: 5]
Eligibility
10+2 passing metrics under formal engineering boards[cite: 5]
ABOUT ARTIFICIAL INTELLIGENCE
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