- 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 / B. Tech. or equivalent 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, Mechatronics, Information Science. Power Electronics and Drives BE / B. Tech.. or equivalent in Electrical
- Admission Selection process for the University quota will be based on RUAS AT scores, while GATE/CUET-PG scores are also accepted and RUAS AT will be exempted for these candidates.
Structure
| Total Fees Per Year |
|---|
| Rs. 213,000 |
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
It is a full-time postgraduate programme focused on Artificial Intelligence and Machine Learning to develop intelligent systems and solutions using advanced computational methods.
Subjects span four semesters, including core AI and ML concepts, electives and project work. Detailed course structures are available per semester, emphasising technical and applied AI domains.
BE/B. Tech or equivalent in disciplines like CSE, ECE, EEE, Mechatronics, Mechanical, Civil, Aerospace, etc., are eligible. Admission is via RUAS AT or GATE/CUET-PG scores.
The total fee is ₹213,000 per year.
Apply online through RUAS Admissions Portal. You may take RUAS AT unless you have GATE or CUET-PG scores.
Graduates can work as AI Engineers, Machine Learning Engineers, Data Scientists, AI Consultants, or Computer Vision Engineers across sectors due to high industry demand.
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