B. Tech. in Artificial Intelligence and Machine Learning
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Overview
There is growing evidence from data that there exists a huge need for professionals and scientists in Artificial Intelligence, Machine learning and Data Sciences. The dearth of professionals coupled with the fact that the fields of Artificial Intelligence, Machine learning and Data Sciences rest upon a rich set of theoretical concepts and practical tools that are foundational in nature clearly indicates that there exists a strong need for a B.Tech. Programme in Artificial Intelligence and Machine learning. The B.Tech Programme in Artificial Intelligence and Machine learning aims to lay the foundational background in computer science to start with, to be inter-disciplinary and to seamlessly integrate courses in Artificial Intelligence, Machine learning and Data Sciences to enable a student to effectively apply the learnings in industry and R&D establishments.
Objective
- Identify and describe various concepts and techniques in AI and Machine Learning
- Explain the underlying logical and engineering principles that govern Artificial Intelligence systems/processes
- Compare and contrast newer approaches and technologies with the existing ones
- Design and synthesise algorithms, architectures and software for AI and Machine Learning systems
- Model, simulate and analyse AI systems
- Modify existing algorithms, architectures and programs to meet newer requirements
- Use AI and Machine Learning Programming environments and tools in practice
- Employ appropriate tools for development of deep learning solutions
- Apply Data Mining techniques in practice
Highlights
Proctoral System
The objective of the Proctoral System is to guide the students periodically on registration for credits, their studies, review of attendance and performance, reporting the attendance and performance to the parents. For every 20 Undergraduate students a Proctor will be nominated from their respective department. Students are advised to interact with their proctor on a regular basis pertaining to matters mentioned above.
Semester Abroad Programme
Through this programme, a B. Tech. student can opt for registering for one semester during his/her final year at a Partner University. The student can register for some courses or an internship or a project at the Partner University.
Innovation Centre
Students, together with faculty have the facility to convert innovative ideas into technology demonstrations. Ideas are assessed based on relevance to society. These projects are then entered in National and International competitions and a select few are registered for patents.
Open Electives
Students can choose from a multitude of open electives across faculties for credits. These are specially designed as a basic introduction which help in the holistic development of an individual.
- Application based learning programme
- Research opportunities
- Internship, training and placement support with various worldwide universities and industries
- State of the art facilities in Computing facilities, Programmeming Laboratories, Hardware and Software tools
- Expert guest lectures from various Institutions and Industries
- Accommodation in student hostels on campus and third party off campus hotels is available
- On campus facilities such as 24/7 Wi-Fi, multiple eateries and laundry facilities make a student’s life comfortable to focus on what matters most.
- Co-curricular Activities are encouraged on campus which include sports events, cultural events both at a university level and a faculty level
Structure
Study Domains
- ES
Engineering Sciences
- Includes an overview into all engineering sciences such as electronics, automotive etc.
- PC
Professional Core
- Deep dive into the core domain of Computer Science and Engineering including Algorithms, Data Structures, Operating Systems such as Linux and Android, Machine learning and popular programming languages such as Python, C, Java etc.
- BS
Basic Sciences
- A comprehensive breadth-wise coverage of Mathematics, Physics and Chemistry
- HSS
Humanities and Social Sciences
- An introduction to professional and personal development, language courses and life skills
- OE
Open Electives
Students can choose from a list of courses offered across the university from multiple disciplines to earn credits
- PSI
Project, Seminar and Internships
- Active participation and output based projects, seminars and internships in industry account for this percentage
- PE
Professional Electives
- Students can choose from subjects offered within Computer Science and Engineering to develop a sharper set of skills in professional core electives such as Internet of Things, Software defined Networks, Quantum Computing or Software Design patterns.
Course Progression
| Course | Credits |
|---|---|
Engineering Mathematics - 1 | 4 |
Engineering Chemistry | 3 |
Elements of Mechanical Engineering and Workshop Practice | 3 |
Elements of Electrical Engineering | 3 |
Elements of Computer Science and Engineering | 3 |
Engineering Chemistry Laboratory | 1 |
Computer Programming Laboratory | 1 |
Basic Electrical Engineering Laboratory | 1 |
Professional Communication | 2 |
Key
- Theory
- Tutorials
- Practicals
| Course | Credits |
|---|---|
Engineering Mathematics-2 | 4 |
Engineering Physics | 3 |
Engineering Mechanics | 3 |
Elements of Electronics Engineering | 3 |
Engineering Drawing | 3 |
Engineering Physics Laboratory | 1 |
Basic Electronics Laboratory | 1 |
Constitution, Human Rights and Law | 2 |
Key
- Theory
- Tutorials
- Practicals
| Course | Credits |
|---|---|
Engineering Mathematics3 | 4 |
Foundations of Machine Learning I | 4 |
Data Structures using Python | 3 |
Principles of Artificial Intelligence | 3 |
Microprocessors and Architecture | 3 |
Basics of Operating Systems | 3 |
Python &Data Structures Laboratory | 1 |
Artificial Intelligence Laboratory | 1 |
Microprocessors Laboratory | 1 |
Environmental Studies | 0 |
Key
- Theory
- Tutorials
- Practicals
| Course | Credits |
|---|---|
Engineering Mathematics4 | 4 |
Foundations of Machine Learning II | 4 |
Programming Paradigms | 4 |
Design and Analysis of Algorithms | 3 |
Machine Learning - I | 3 |
Machine Learning Laboratory | 1 |
Programming Paradigms Laboratory | 1 |
Key
- Theory
- Tutorials
- Practicals
| Course | Credits |
|---|---|
Data Mining | 4 |
Database Systems | 3 |
Machine Learning - II | 4 |
Computer Networks | 3 |
Innovation Course 1 | 3 |
IoT - 1 | 1 |
Database Systems Laboratory | 1 |
Computer Networks Laboratory | 1 |
Economics and Cost Estimation in Computer Engineering | 2 |
Key
- Theory
- Tutorials
- Practicals
| Course | Credits |
|---|---|
Graph Theory and Optimization | 3 |
Computer Vision | 4 |
Innovation Course II | 3 |
Natural Language Processing | 3 |
Deep Learning and Applications | 3 |
Distributed and Cloud Computing | 4 |
Pattern recognition | 3 |
Natural Language Processing Laboratory | 1 |
Deep Learning and Applications Laboratory | 1 |
Key
- Theory
- Tutorials
- Practicals
| Course | Credits |
|---|---|
Professional Core Elective - 1 | 4 |
Professional Core Elective - 2 | 3 |
Professional Core Elective - 3 | 3 |
Open Elective | 3 |
I] Project Work - I II]Internship (Choose one) | 4 |
Seminar | 1 |
Key
- Theory
- Tutorials
- Practicals
| Course | Credits |
|---|---|
Open Elective - 2 | 3 |
Project work 2 | 8 |
Key
- Theory
- Tutorials
- Practicals
Details
Teaching and Assessment:
During each semester, students’ performance is assessed through Continuous Evaluation (CE) and a Semester End Examination (SEE). CE and SEE weightages carry equal weightage.
Students are awarded grades based on the marks scored. View complete Grading System for the current academic batch.
Key Skill Development:
- Ability to apply concepts of Machine learning to real-life problems
- Ability to apply problem solving approaches to real-life computer programming scenarios and to build complex and large scale AI enabled software systems
- Ability to specify appropriate abstractions of complex requirements and design effective solutions in the form of reliable algorithms and computer programs
- Ability to analyse engineering problems, interpret data and arrive at meaningful conclusions involving mathematical inferences
- Ability to design an engineering system, component, or process to meet desired needs considering public health and safety, and the cultural, societal, and environmental considerations
- Ability to understand the effect of engineering solutions on legal, cultural, social and public health and safety aspects
- Ability to develop sustainable solutions and understand their effect on society and environment
- Ability to work as a member of a team, to plan and to integrate knowledge of various engineering disciplines and to lead teams in multidisciplinary settings
- Ability to adapt to the changes and advancements in technology and engage in independent and life-long learning
- Ability Enhancement Compulsory Courses (AECC) such as Professional Communication, Constitution, Human Rights and Law, and Environmental Studies
Careers
Entrepreneur
Researcher
AI professional
Artificial Intelligence
Machine Learning
Admissions
Eligibility
Indian Nationals
- Passed 10+2 examination with Physics and Mathematics as compulsory courses along with Chemistry/Bio-technology/ Biology /Electronics /Computer science.
- Obtained at least 45% aggregate marks (40% in case of candidates belonging to reserved category) in the above mentioned courses.
Application Process
Through RUAS-AT
- Take the RUAS-AT Entrance Test
- Admission counselling for selection
- Once selected, students need to fill in the admission form
- Pay tuition fee
- Submit documents to the University
Through CET/ ComedK
- CET Code E235, ComedK Code
- Counselling and Selection
- Download the admission form
- Submit completed form and documents at the University Admissions Office
- Pay fees online or through DD at the University admission Office. See full instructions.
Other accepted Scores
- Contact Director Admissions
Fees & Scholarships
Government Seats:
- As per GoK
University Seats:
- Refer Fee Structure
Eligibility
International Students/OCI/ NRI/ SAARC
- Foreign students should have 10+2 equivalent qualification approved by Association of Indian Universities
- Should have proof of proficiency in English with a minimum TOEFL score of 8
Note: Up to 15% of seats are reserved for foreign / NRI Students
Application Process
Through RUAS-AT
- Take the RUAS-AT Entrance Test
- Admission counselling for selection
- Once selected, students need to fill in the admission form
- Pay tuition fee
- Submit documents to the University
Through CET/ ComedK
- CET Code E235, ComedK Code
- Counselling and Selection
- Download the admission form
- Submit completed form and documents at the University Admissions Office
- Pay fees online or through DD at the University admission Office. See full instructions.
Other accepted Scores
- Contact Director Admissions
Fees & Scholarships
NRI/ Foreign Students:
- USD 6000 + Other fee Rs.38,550 per annum
Eligibility
Lateral Entry into 3rd Semester
(In all cases of Lateral Entry admissions, the Equivalence Committee decision will be final.)
- Passed Diploma examination from an approved institution with a minimum of 45% marks (40% in case of candidates belonging to reserved category) in appropriate branch of Engineering /Technology
- Passed B.Sc. from a University recognized by UGC with a minimum of 45% marks (40% in case of candidates belonging to reserved category) and passed 12th standard with Physics and Mathematics as courses.
Application Process
Through RUAS-AT
- Take the RUAS-AT Entrance Test
- Admission counselling for selection
- Once selected, students need to fill in the admission form
- Pay tuition fee
- Submit documents to the University
Through CET/ ComedK
- CET Code E235, ComedK Code
- Counselling and Selection
- Download the admission form
- Submit completed form and documents at the University Admissions Office
- Pay fees online or through DD at the University admission Office. See full instructions.
Other accepted Scores
- Contact Director Admissions
Fees & Scholarships
Government Seats:
- As per GoK
University Seats:
- Refer Fee Structure
Start your journey with RUAS
Downloads
- B.Tech Programme Regulations 2019 pdf | 1.2 MB
- B.Tech (AIML) Programme Specifications 2020 pdf | 1.7 MB
Contact
- Director - Admissions
- Dr. T. Hemanth
- Phone
- 80 4536 6616
- email hidden; JavaScript is required
Programmes
- M.Tech. in Data Science and Engineering
- M.Tech in Artificial Intelligence & Machine Learning
- M. Tech. in Power Electronics and Drives
- B.Tech. in Electrical and Electronics Engineering
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- B.Tech. in Electronics and Communication Engineering
- B.Tech. in Information Science and Engineering
- B.Tech. in Mathematics and Computing
- B.Tech. in Mechanical Engineering
- B.Tech. in Robotics
- B.Tech. in Civil Engineering
- M.Tech. in Construction Engineering and Management
- M.Tech. in Environmental Engineering and Management
- M.Tech. in Transportation Engineering
- M. Tech. in Automotive Engineering
- B.Tech. in Aerospace Engineering
- B.Tech. in Automotive Engineering
- M.Tech. in Aerospace Engineering
- B.Tech. in Computer Science and Engineering
- B. Tech. in Artificial Intelligence and Machine Learning
- M. Tech. in Advanced Machinery Design
- M. Tech. in Manufacturing Technologies and Engineering Management
- M.Tech. in Robotic Engineering
- M.Tech. in VLSI and Nanotechnology