B. Tech. in Mathematics and Computing
. The department started a four year B.Tech. program in Mathematics and Computing from Academic year 2019-2020.
. Admission to B.Tech. program in Mathematics and Computing is through JEE (Advanced) 2019-2020.
Aim of Progam
The primary objective of the Department of Artificial Intelligence and Data Engineering (AIDE) is to promote a deep understanding and proficiency in artificial intelligence and data science, particularly from an engineering perspective. AIDE places a strong emphasis on early immersion in key concepts such as programming, AI, and data science right from the beginning of its courses. Additionally, the program introduces control systems with a focus on their application in advanced AI scenarios, including robotics and autonomous vehicles.
The curriculum is meticulously designed to support an interdisciplinary approach, closely aligned with industry requirements in AI and data science. This is further reflected in the selection of electives, which are predominantly tailored to align with recent advancements and practical use cases in AI technologies. This structure ensures that students are well-equipped with the relevant skills and knowledge to excel in the rapidly evolving field of AI and data engineering.
Curriculum Focus
AIDE: The curriculum emphasizes programming, machine learning, data analysis, intelligent and control systems development. It includes courses in machine learning, neural networks, deep learning, multimodal learning, data visualization, big data analytics, and advanced statistics. Most assignments and lab sessions are designed around practical AI and Data Engineering use cases. Both minor and major projects are geared towards addressing real-world industry challenges using AI and Data Engineering solutions.
AIDE Mission
The mission of the AIDE Department is to provide a comprehensive educational experience that equips students with deep knowledge and practical skills in artificial intelligence and data science. It aims to foster research excellence, encourage interdisciplinary collaboration, and develop professionals who can innovatively address complex challenges in the tech industry and beyond.
AIDE Vision
The vision of the Artificial Intelligence and Data Engineering (AIDE) Department is to be a leader in advancing the fields of AI and data engineering through innovative education and cutting-edge research, preparing students to excel in the evolving technological landscape and contribute significantly to industry and society.
Semester-wise Course Structure: |
Semester - 1 |
Sr. | Course Code | Course Name | Credits |
Total Credits | 18.5 |
Semester - 2 |
Sr. | Course Code | Course Name | Credits |
Total Credits | 18 |
Semester - 3 |
Sr. | Course Code | Course Name | Credits |
Total Credits | 18 / 18.5 |
Semester - 4 |
Sr. | Course Code | Course Name | Credits |
Total Credits | 19 / 19.5 |
Semester - 5 |
Sr. | Course Code | Course Name | Credits |
Total Credits | 16 / 17.5 |
Semester - 6 |
Sr. | Course Code | Course Name | Credits |
Total Credits | XX |
Summer Vacation following Semester 6 |
Sr. | Course Code | Course Name | Credits |
1. | II301 | Industrial Internship and Comprehensive Viva Voce (70% weightage for 8-week full internship and 30% for comprehensive viva on program fundamentals) | 3.5 |
Total Credits | 3.5 |
Semester - 7 |
Sr. | Course Code | Course Name | Credits |
ELECTIVE COURSES |
Total Credits | 15 |
Semester - 8 |
Sr. | Course Code | Course Name | Credits |
ELECTIVE COURSES |
Total Credits | 15 |
XXXXX denotes Open Elective Course |
Grand Total: 145 |
- Data Mining
- Deep Learning
- Financial derivatives Pricing
- Time Series Analysis
- Graph Theory
- Number Theory & Cryptography
- Functional Analysis
- Mathematical Image Processing
- Computational Partial Differential Equations
- Randomized Algorithms
- Game Theory
- Evolutionary Game Theory
- Stochastic Process and Monte Carlo Simulation
- Applied Statistics
- Operating systems
- Data Base Management System
- Computer Architecture
- Approximation Algorithm
- Fuzzy Logic & Application
- Matrix Computation
- Applied Linear Algebra
- Complex Analysis
- Dynamical Systems
- Artificial Intelligence
- Algorithmic graph theory
- Combinatorial optimization
- Graphical Models
- Computer Vision
- Network Science
- Computational Fluid Dynamics
- Fluid Dynamics
- Advanced Data Structures