Shape the Future with AI & ML!
The B.Sc. in Artificial Intelligence & Machine Learning at Osmania University is a dynamic three-year undergraduate program designed to equip students with the skills and knowledge needed to thrive in the rapidly evolving world of intelligent technologies. Offered under the CBCS (Choice Based Credit System), this program seamlessly blends core computer science principles with specialized training in AI and ML, preparing students for rewarding careers in research, industry, and academia.
Students begin by building a strong foundation in computer science, developing essential problem-solving and programming skills through hands-on learning in programming, data structures, and algorithms.
As they progress, students dive into the exciting world of Artificial Intelligence and Machine Learning, exploring AI problem-solving, search strategies, knowledge representation, and machine learning techniques. The curriculum includes engaging modules on intelligent agents, neural networks, expert systems, and heuristic learning methods, offering both theoretical insights and practical, real-world applications.
This program not only emphasizes technical expertise but also encourages innovation, critical thinking, and a forward-looking approach, ensuring graduates are ready to lead in the era of intelligent technologies.
| Year | Sem | Paper code | Theory/ Practical | Paper Title | Credi ts | Work Load (Hours I Week) |
Total Marks |
|---|---|---|---|---|---|---|---|
| I | I | BS-101-T | Paper-I | Descriptive Statistics and Probability | 4 | 4 | 100 |
| BS-101-P | Practical -I | Basic Statistical Analysis Lab using Excel & R |
1 | 2 | 25 | ||
| II | BS-202-T | Paper-II | Probability Distributions | 4 | 4 | 100 | |
| BS-202-P | Practical -II | Probability Distributions Lab using Excel & R |
1 | 2 | 25 | ||
| II | III | BS-303-T | Paper-III | Statistical Inference | 4 | 4 | 100 |
| BS-303-P | Practical-III | Statistical Inference lab using Excel & R | 1 | 2 | 25 | ||
| BS-303-SE | SEC-2 | Subject specified | 2 | 2 | 50 | ||
| IV | BS-404-T | Paper-IV | Analysis of Correlation, Regression and Basic Experimental Designs | 4 | 4 | 100 | |
| BS-404-P | Practical-IV | Analysis of Correlation, Regression and Basic Experimental Designs Lab | 1 | 2 | 25 | ||
| BS-404-SE | SEC-4 | Subject specified | 2 | 2 | 50 | ||
| III | v | BS-505-T | Paper-V | Sampling Theory & Operation Research | 4 | 4 | 100 |
| BS-505-P | Practical-5 | Sampling Theory & Operation Research Lab | 1 | 2 | 25 | ||
| BS-505-GE | Generic Elective | Statistical Analysis | 4 | 4 | 100 | ||
| VI | BS-606-T | Paper-VI | Industrial Statistics | 4 | 4 | 100 | |
| BS-606-P | Paper-VI | Industrial Statistics | 1 | 2 | 25 | ||
| BS-606-0 | Optional | Data Analysis Project | 4 | 4 | 100 |
| Course Title | Hours/Week | Credits | |
|---|---|---|---|
| Theory | Practical | ||
| Semester-I | |||
| Fundamentals of Information Technology | 4 | 3 | 4+1=5 |
| Semester -II | |||
| Object Oriented Programming with Python | 4 | 3 | 4+1=5 |
| Semester -III | |||
| Operating Systems with Linux | 4 | 3 | 4+1=5 |
| Semester -rv | |||
| Data Analytics | 4 | 3 | 4+1=5 |
| Semester-V | |||
| Artificial Intelligence | 4 | 3 | 4+1=5 |
| Semester-VI | |||
| Machine Leaming | 4 | 3 | 4+1=5 |
| Year | Paper | Semester | Subject | Hours/PerWeek | Credits | Marks (IA) | Marks (EE) | Total Marks | |
|---|---|---|---|---|---|---|---|---|---|
| Theory | Tutorials | ||||||||
| 1 | DSCI | I | Differential Equations | 5 | 1 | 5 | 25 | 100 | 125 |
| DSCII | II | RealAnalysis | 5 | 1 | 5 | 25 | 100 | 125 | |
| 2 | DSCIII | III | Differential & Vector Calculus | 5 | 1 | 5 | 25 | 100 | 125 |
| DSCIV | IV | Algebra | 5 | 1 | 5 | 25 | 100 | 125 | |
| 3 | DSCV | V | LinearAlgebra | 5 | 1 | 5 | 25 | 100 | 125 |
| DSEVI | VI | (A) Numerical Analysis OR (B) Integral Transforms OR (C) Analytical SolidGeometry |
5 | 1 | 5 | 25 | 100 | 125 | |
| 3 | SEC4 | VI | NumberTheory OR Quantitative Aptitude |
2 | - | 2 | 10 | 40 | 50 |
| 3 | MDC | V | Basic Mathematics (Multi - Discipline) |
4 | - | 4 | 20 | 80 | 100 |
Specialized Roles with High “Moats” :
A “moat” is a skill that is hard to learn, making you harder to replace.
Healthcare AI Integrator: A massive growth sector. These professionals specialize in FDA-compliant AI, medical imaging, and drug discovery.
AI Product Manager: Bridges the gap between what’s technically possible and what users actually need
Graduates can pursue M.Sc., M.Tech, or Ph.D. in AI, Machine Learning, Data Science, or related fields, opening pathways to research, academia, and advanced industry roles.
Opportunities also exist in specialized domains such as Computer Vision, Natural Language Processing, Deep Learning, Robotics, and Big Data Analytics.