Passed Standard XII (10+2) or equivalent examination with Physics and Mathematics as compulsory subjects along with one of the Chemistry/ Biotechnology/ Biology/ Technical Vocational subject/ Computer Science/ Information Technology/ Informatics Practices/ Agriculture/ Engineering Graphics/ Business Studies from any recognized Board with a minimum of 50% marks or equivalent grade (45% marks or equivalent grade for Scheduled Caste/ Scheduled Tribes).
The Bachelor of Science in Artificial Intelligence and Machine Learning (B.Sc (AI & ML)) and B.Sc (AI & ML) Honors and Research; program is designed to provide advanced education and training in the field of AI and ML. Driven by the combination of increased access to data, computational power, and improved algorithms, Artificial Intelligence (AI) technologies are entering the mainstream of technological innovation. These technologies include search, machine learning, natural language processing, robotics and computer vision
M.Sc. AI, M.Sc. Data Science, M.Sc. ML, M.Sc. Computer Science, M.Sc. Computer Appliance
Machine Learning Engineer, Data Analyst, AI Research Assistant, Data Scientist, AI Solutions Architect, Business Intelligence (BI) Developer, Deep Learning Engineer, AI Researcher, Chief Data Scientist or Chief AI Officer
Semester-I
| Course Code | Course Name |
|---|---|
| AIML-101-T | Object Oriented concepts and Programming using C++ |
| AIML-102-P | Practical based on AIML-101-T |
| MTS-101-T | Discrete Structures for Computer Science |
| MTS-102-P | Practical based on MTS-101-T |
| STS-101-T | Notion of Statistical Data Analysis |
| STS-102-P | Practical based on STS-101-T |
| OE-101-AIML-T | From University Basket* |
| SEC-101-AIMLT | Basic Probability theory and Discrete Distributions |
| AIML-101-IKS | Generic IKS |
| AEC-101-ENG | English |
| VEC-101-ENV | EVS-I |
Semester-II
| Course Code | Course Name |
|---|---|
| AIML-151-T | Introduction to Python Programming |
| AIML-152-P | Practical based on AIML-151-T |
| MTS-151-T | Graph Theory |
| MTS-152-P | Practical based on MTS-151-T |
| STS-151-T | Continuous Probability Distributions and Testing of Hypothesis |
| STS-152-P | Practical based on STS-151-T |
| OE-151-AIML-T | From University Basket* |
| SEC-151-AIML-T | Databases – I |
| AEC-151-ENG | English |
| VEC-151-ENV | EVS-II |
| CC-151-PE/ NSS /NCC | From University Basket |
Semester-III
| Course Code | Course Name |
|---|---|
| AIML-201-MJ-T | Data Structures (using Python) |
| AIML-202-MJ-T | Software Engineering |
| AIML-203MJ-P | Lab course on AIML201MJ + AIML202MJ |
| AIML-221-VSC-P | Advanced Python Programming |
| AIML-201-IKS | Computing in Ancient India |
| AIML-231-FP | Mini Project |
| AIML-241-MN-T | Linear Algebra |
| AIML-242-MN-P | Practical on AIML241MN |
| OE-201-AIML-T | From University Basket* |
| AEC-201-ENG | From University Basket |
| CC-201-PE/NSS /NCC | From University Basket |
Semester-IV
| Course Code | Course Name |
|---|---|
| AIML-251-MJ-T | Microservices using Python |
| AIML-252-MJ-T | Artificial Intelligence - I |
| AIML-253-MJ-P | Practical based on AIML251MJ + AIML252M |
| AIML-221-VSC-P | Databases – II |
| AIML-241-FP | Mini Project |
| AIML-241-MN-T | Logic |
| AIML-242-MN-P | Practical based on AIML241MN |
| OE-251-AIML-T | From University Basket* 2 |
| SEC-251-AIML-P | DAA - I (Brute Force, D&C, Greedy, Dynamic Programming) |
| AEC-251-SUB | From University Basket |
| CC251PE/NSS/NCC | From University Basket |
Semester-V
| Course Code | Course Name |
|---|---|
| AIML-301-MJ-T | Artificial Intelligence - II |
| AIML-302-MJ-T | Machine Learning Techniques – I (Supervised) |
| AIML-303-MJ-T | Data Preparation and Visualization |
| AIML-304-MJ-T | DAA - II (Backtracking, B&B, Randomized, P&NP and approximation algos) |
| AIML-305-MJ-P | Practical based on AIML301MJ |
| AIML-306-MJ-P | Practical based on AIML302MJ & AIML303MJ |
| AIML-310-MJ-T | Big Data Analytics |
| AIML-311-MJ-P | Practical based on AIML306MJ |
| OR | |
| AIML-312-MJ-T | MEAN – I |
| AIML-313-MJ-P | Practical based on AIML308MJ |
| OR | |
| AIML-314-MJ | Mobile app development |
| AIML-315-MJ-P | Practical based on AIML310MJ |
| AIML-321-VSC-P | Linux Shell Scripting |
| AIML-331-FP | Project |
| AIML-341-MN-T | Calculus for ML |
Semester-VI
| Course Code | Course Name |
|---|---|
| AIML-351-MJ-T | Optimization Techniques |
| AIML-352-MJ-T | Machine Learning Techniques – II |
| AIML-353-MJ-T | Data Mining Techniques |
| AIML-354-MJ-T | Evolutionary Algorithms (FL, GA) |
| AIML-355-MJ-P | Practical based on AIML351MJ |
| AIML-356-MJ-P | Practical based on AIML352MJ & AIML353MJ |
| AIML-360-MJ-T | Business Intelligence (Atoti) |
| AIML-361-MJ-P | Practical based on AIML357MJ |
| OR | |
| AIML-362-MJ-T | MEAN II |
| AIML-363-MJ-P | Practical based on AIML359MJ |
| OR | |
| AIML-364-MJ-T | Game Theory |
| AIML-365-MJ-P P | Practical based on AIML361MJ |
| AIML-371-VSC-P | Database Technologies (Unstructured Databases) |
| AIML-381-OJT | On Job Training |