BTech in AI & Data Analytics
We are delighted to introduce our newly minted BTech course in AI & Data Analytics (AIDA), an exciting journey into the realms of data science and artificial intelligence. BTech AIDA is meticulously crafted to equip students with the key skills and knowledge necessary to thrive in the dynamic landscape of modern technology. This unique programme is designed to cultivate expertise in diverse facets of AI and data analytics, offering a panoramic view of its applications across industries.
Key Learning Outcomes
At the heart of AIDA lie nine key outcomes, each serving as a pillar upon which students will build their proficiency:
Mathematical Foundations
Delve deep into the mathematical underpinnings of data science and artificial intelligence, laying a robust groundwork for advanced analysis and modelling.
ML / AI Models
Explore a spectrum of models, ranging from mathematical and statistical to network architectures, empowering students to develop sophisticated solutions to complex problems.
Learning Algorithms and Statistical Inferencing
Master the art of algorithmic design and statistical inference, essential for extracting meaningful insights from vast datasets.
Programming Skills
Hone programming skills tailored for crafting cutting-edge data science and AI solutions, leveraging the latest tools and languages.
Data Acquisition and Pre-processing
Learn the intricacies of acquiring, pre-processing, and curating data, essential for ensuring its quality and relevance in analytical endeavors.
Systems Thinking
Cultivate a systems thinking mindset crucial for deploying machine learning solutions effectively within real-world contexts.
Mathematical Modeling and Simulation
Harness the power of mathematical modelling, computational methods, and simulation techniques to simulate and analyze complex systems.
Application to Real-world Problems
Apply data analytics and AI techniques to tackle real-world challenges across diverse domains, fostering innovation and impact.
Fair and Responsible AI
Embrace the principles of fairness and responsibility in AI development, ensuring ethical and equitable deployment of technology.
AIDA offers flexibility, allowing students to tailor their learning journey through a wide range of electives. From Speech & Language Technology and Computer Vision to Applications in Control & Detection and Time-Series Analysis, students can explore their personal passions while building expertise in their chosen domains.
The core curriculum provides a comprehensive foundation in AI and data analytics, covering essential topics such as linear algebra and calculus, machine learning, deep learning, and reinforcement learning. This robust toolkit ensures students are well-equipped to tackle complex challenges in the field.
Furthermore, practical experience is ingrained into the curriculum through laboratory sessions, workshops, and real-world projects, ensuring students graduate not only with theoretical knowledge but also with hands-on expertise ready for immediate application in the industry.
Join us on this transformative journey into the realm of AI and data analytics, where innovation knows no bounds, and the possibilities are limitless. Welcome to AIDA, where the future is waiting to be shaped by your brilliance!
Detailed Curriculum
Semester I
| Course | Type | Category | Credits |
|---|---|---|---|
| Foundations of Linear Algebra | Core | Science | 9 |
| Calculus for Engineers | Core | Science | 9 |
| Programming and Data Structures | Core | Computation | 9 |
| Programming Laboratory | Core | Computation | 6 |
| Basics of Engineering Principles | Core | Engineering | 9 |
| Life Skills I | Core | General | 4 |
| Ecology and Environment | Core | General | 2 |
| NSO / NCC / NSS | Elective | General | 2 |
| Recreation I | Elective | General | 2 |
| Workshop I | Core | Engineering | 3 |
Semester II
| Course | Type | Category | Credits |
|---|---|---|---|
| Introduction to Computational Chemistry | Core | Science | 9 |
| Probability & Statistics for Engineers | Core | Science | 10 |
| Optimization for Engineers | Core | Engineering | 9 |
| Optimization Lab* | Core | Engineering | 6 |
| Life Skills II | Core | General | 2 |
| Recreation II | Core | General | 2 |
| NSO / NCC / NSS | Elective | General | 2 |
| Computational Methods for DS | Core | Professional | 10 |
Semester III
| Course | Type | Category | Credits |
|---|---|---|---|
| Introduction to Computational Physics | Core | Science | 9 |
| Machine Learning I | Core | Professional | 9 |
| Machine Learning Lab | Core | Professional | 6 |
| Introduction to Computational Biology | Core | Science | 9 |
| Data Curation and Visualization | Core | Professional | 9 |
| Entrepreneurship Course | Core | Management | 9 |
Semester IV
| Course | Type | Category | Credits |
|---|---|---|---|
| Algorithms for Data Science | Core | Professional | 9 |
| Introduction to Computer Systems | Core | Professional | 9 |
| Artificial Intelligence | Core | Professional | 9 |
| AI Lab | Core | Professional | 6 |
| Physics Elective | Elective | Science | 9 |
| Free (Unallotted) Elective | Elective | Free | 9 |
Semester V
| Course | Type | Category | Credits |
|---|---|---|---|
| Databases | Core | Professional | 9 |
| Machine Learning II | Core | Professional | 9 |
| ML ops Lab | Core | Professional | 6 |
| Core Basket - I | Elective | Professional | 9 |
| Dept. Elective | Elective | Professional | 9 |
| Deep Learning | Core | Professional | 9 |
| DL Lab | Core | Professional | 6 |
Semester VI
| Course | Type | Category | Credits |
|---|---|---|---|
| Free (Unallotted) Elective | Elective | Free | 9 |
| Free (Unallotted) Elective | Elective | Free | 9 |
| Humanities Course | Elective | Humanities | 9 |
| Free (Unallotted) Elective | Elective | Free | 9 |
| Free (Unallotted) Elective | Elective | Free | 9 |
Semester VII
| Course | Type | Category | Credits |
|---|---|---|---|
| Online & Reinforcement Learning | Core | Professional | 9 |
| Core Basket - II | Elective | Professional | 9 |
| Responsible AI | Core | Professional | 9 |
| Dept. Elective | Elective | Professional | 9 |
| Humanities Elective | Elective | Humanities | 9 |
| Project I | Core | Professional | 9 |
Semester VIII
| Course | Type | Category | Credits |
|---|---|---|---|
| Project II / Elective | Elective | Professional | 18 |
| Elective | Elective | Free | 9 |
| Professional Ethics | Core | General | 2 |
| Humanities Elective | Elective | Humanities | 9 |