- Faculty
Faculty of Engineering and Technology
- Department
Department of Computer Science and Engineering
- Campus
Technology Campus (Peenya Campus)
- Engagement Mode
Full Time
- Study
B.Tech or B.E Students Minimum 2 Years ,Maximum 4 Years.
Overview
As per an NQR report, the estimated value of the worldwide Artificial Intelligence (AI) market stood at approximately US$ 119.78 billion in 2022. Forecasts indicate a substantial surge, projected to reach around US$ 1,591.03 billion by 2030. This growth trajectory reflects a recorded Compound Annual Growth Rate (CAGR) of 38.1% from 2022 to 2030.
The pursuit of an MTech in Artificial Intelligence and Machine Learning is an exciting journey into the realm of cutting-edge technology. This specialised programme delves deep into the intricacies of AI, focusing on its multifaceted applications across various industries. It offers a comprehensive understanding of machine learning techniques, enabling individuals to create intelligent systems capable of learning from data.
The MTech Artificial Intelligence and Machine Learning programme amalgamates theoretical knowledge with practical expertise, preparing graduates to tackle the complexities of today's AI-driven world and contribute significantly to innovation and technological advancement. Pursuing an MTech in Artificial Intelligence equips students with advanced skills in data analysis, neural networks, and algorithm development.
Program Objectives
- A renowned establishment with a 62-year history
- Accredited with NAAC A+ and ranked by NIRF
- Faculty consisting of experts from prestigious institutions like IITs, IIMs, and more
- Outstanding placement records, including the highest domestic package reaching Rs 45 LPA
- Partnerships established with leading global universities such as MIT, University of Illinois, among others
- State-of-the-art facilities including Advanced Learning Centre, Techno Center, NABL accredited Labs, Incubation Centre, etc.
Curriculum Details
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | 19MIC501A | Mathematics for Machine Learning | 3 | 2 | 4 | 100 | |
| 2 | 19DCS501A | Programming for Data Science | 3 | 2 | 4 | 100 | |
| 3 | 19DSC502A | Data Mining | 3 | 2 | 4 | 100 | |
| 4 | 19MIC502A | Artificial Intelligence | 3 | 2 | 4 | 100 | |
| 5 | 19MIE501A | Professional Elective-1 | 3 | 2 | 4 | 100 | |
| 6 | 19FET509A | Research Methodology & IPR | 2 | - | - | 2 | 50 |
| 7 | 19FET510A | Audit Course | 1 | - | - | 0 | 0 |
| Total | 18 | 4 | 6 | 22 | 550 | ||
| Total no. of Hours per Week | 38 | ||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
| 1 | 19MIC504A | Artificial Neural Networks | 3 | 2 | 2 | 5 | 100 |
| 2 | 19MIC505A | Pattern Recognition | 3 | 2 | 2 | 5 | 100 |
| 3 | 19MIE502A | Professional Elective - 2 | 3 | 2 | 4 | 100 | |
| 4 | 19MIE503A | Professional Elective - 3 | 3 | 2 | 4 | 100 | |
| 5 | 19MIE504A | Professional Elective 4 | 3 | 2 | 4 | 100 | |
| 6 | 19FET520A | Audit Course | 1 | 0 | |||
| Total | 16 | 8 | 6 | 22 | 500 | ||
| Total no. of Hours per week | |||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | 19MIC521A | Internship | 10 | 4 | 100 | ||
| 2 | 19MIC522A | Group Project | 15 | 8 | 200 | ||
| Total | - | - | 25 | 12 | 300 | ||
| Total number of Contact hours per week | 25 hours | ||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | 19MIC523A | Dissertation and Publication | - | - | 24 | 24 | 400 |
| Total | 24 | 24 | 400 | ||||
| Total number of Contact hours per week | 24 hours | ||||||
Note:
Students are required to select Professional Elective course in the 1st Semester and 2nd Semester, from Elective list given as follows:
| Stream / Specialization | S.No | Course Code | Course Title |
|---|---|---|---|
| AI for Healthcare | 1 | 19MIE501A | Computational Intelligence |
| 2 | 19MIE502A | Deep Learning | |
| 3 | 19MIE503A | Probabilistic Graphical Models | |
| 4 | 19MIE504A | AI for Healthcare | |
| 5 | 20MIE507A | Advanced Machine Learning | |
| AI for Robots | 1 | 19MIE505A | Computer Vision |
| 2 | 19MIE506A | AI for Robots | |
| 3 | 20MIE508A | Reinforcement Learning | |
| 4 | 19MIE501A | Computational Intelligence | |
| 5 | 19MIE502A | Deep Learning |
Eligibility Criteria
BE or B.Tech or equivalent degree in:
Electronics and Communication Engineering
Computer Science and Engineering
Electrical and Electronics Engineering
Automobile / Automotive Engineering
Mechanical Engineering
Aerospace / Aeronautics Engineering
Civil Engineering
Bio-Medical Engineering
Mechatronics
Information Science
Telecommunication Engineering
Instrumentation Engineering; or
Medical Electronics
Candidates who belong to SC/ST categories are given a different qualifying mark as per government notifications.
Candidates should have the equivalent qualification approved by the Association of Indian Universities.
Should have proof of proficiency in English with a minimum TOEFL score of 8.
Structure
| Tuition Fees | University Fees | Total Fees |
|---|---|---|
| 175000 | 40000 | 215,000 |
Intake
Artificial Intelligence and Machine Learning 18 students.
Career Path
- Machine Learning Engineer
- AI Engineer
- AI Data Annotation Expert
- AI Solutions Architect
- AI Consultant
- Data Scientist
- Computer Vision Engineer
FAQs
Yes. As per the UGC Reference Letter No: F 8 - 22/2013(CPP1/PU) dated 21 July, 2016, Ramaiah University of Applied Sciences is recognised by the University Grants Commission.
Yes. Ramaiah University of Applied Sciences is approved by the AICTE. Refer to Approval Reference Number F. No. South-West/2020 – 21/1 – 7161814455, dated 30 June 2020.
RUAS has 10 Faculties that together offer more than 90+ programmes in different fields of study and of different levels. For programme-specific details.
At RUAS, students can apply for a programme online or in person. For details on how to apply online
The fee structure for programmes differs based on the level of the programme and the student’s category.
As per the rules of the University, the entire fee amount must be collected at the beginning of the year and cannot be paid per semester.
The Gnanagangothri Campus and the Technology Campus have well-planned hostel facilities for students. However, the on-campus accommodation at Technology Campus is currently restricted to female students only.
Students from both campuses can also opt for private, off-campus accommodation.
Once you have applied for a programme, you will be given login credentials like a User ID and a Password. Using these, you can then log in and choose the day and time of your test from the given options. For more details and instructions.
While the actual date varies every year, admissions for UG programmes close in the month of August, while those for PG programmes close in September.
The Faculties of Engineering & Technology, Art & Design, and Mathematical & Physical Sciences are housed in the Technology Campus. Therefore, all programmes offered by these Faculties are conducted in the Technology Campus.
Similarly, the Gnanagangothri Campus on New BEL Road is home to the Faculties of Dental Sciences, Pharmacy, Hospitality Management and Catering Technology, Management & Commerce, Life & Allied Health Sciences, School of Social Sciences and School of Law and the programmes offered by each of them
No. While MSRIT is an autonomous college under VTU, RUAS is a private university. Both institutions are, however, managed by the same group.
Yes. The University offers students scholarships. Students can also opt for scholarships offered by the state and central governments, or by private organisations and charities.
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