- Faculty
Faculty of Engineering and Technology
- Department
Department of Computer Science and Engineering
- Campus
Technology Campus (Peenya Campus)
- Engagement Mode
Full Time
- Study
P.U.C students Minimum 4 Years ,Maximum 8 Years, Diploma students Minimum 3 Years, Maximum 6 Years.
Overview
According to Next Move Strategy Consulting, the market for artificial intelligence (AI), currently valued at nearly 100 billion U.S. dollars is is projected to grow twentyfold by 2030, reaching almost two trillion U.S. dollars. The number of AI-related job openings is projected to reach 2.3 million globally by 2023, indicating a high demand for B. Tech. Artificial Intelligence and Machine Learning professionals in the coming era
The escalating demand for B Tech Artificial Intelligence and Machine Learning experts underscores the significance of pursuing a B Tech in this field. At M. S. Ramaiah University of Applied Sciences (RUAS), the B Tech Artificial Intelligence and Machine Learning syllabus intricately details course essentials, equipping students with the knowledge and skills required to excel in these cutting-edge technologies. Explore all the B Tech Artificial Intelligence course details of RUAS and embark on a transformative journey toward expertise in AI and ML.
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
Programme Educational Outcomes (PEOs)
- Provide students with a strong foundation in mathematics and computing along with breadth and foundational requirement in computing, science, engineering and humanities to enable them to devise and deliver efficient and safe solutions to challenging problems in Computer Science and inter-disciplinary areas
- Impart analytic and cognitive skills required to develop innovative solutions for R&D, to build creative, dependable and safe products for Industry based on dynamic societal requirements motivated and nurtured by sound theoretical and practical knowledge of time tested and long lasting principles of computer science, current tools and technologies
- Develop managerial and entrepreneurial skills inculcating strong human values along with social, interpersonal and leadership skills required for professional success in evolving global professional environments
Programme Outcomes (Pos)
- Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems
- Identify, formulate, review research literature, and analyse complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences
- Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations
- Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions
- Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities with an understanding of the limitations
- Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice
- Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development
- Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice
- Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings
- Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions
- Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments
- Recognize the need for and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change
Programme Outcomes (Pos)
- Apply principles and best practices in design of efficient algorithms and correct programs; build reliable, secure and robust software, making use of knowledge of computer architecture, systems software, networking, Web technologies distributed computing
- Use knowledge gained in both breadth courses in science and engineering and depth courses in mathematics and computing, solving problems of relevance to society, industry and R&D in an innovative manner
- Engage in lifelong learning by applying knowledge of fields of computer science and refining it and evangelizing applications and technologies to all interested communities
Curriculum Details
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max Marks |
|---|---|---|---|---|---|---|---|
| 1 | MTB101A | Engineering Mathematics 1 | 3 | 1 | 0 | 4 | 100 |
| 2 | PYB102A | Engineering Physics and Laboratory | 3 | 0 | 2 | 4 | 100 |
| 3 | CEF101A | Engineering Mechanics | 3 | 0 | 0 | 3 | 100 |
| 4 | ECF102A | Elements of Electronics Engineering and Laboratory | 3 | 0 | 2 | 4 | 100 |
| 5 | MEF103A | Engineering Drawing | 2 | 0 | 2 | 3 | 100 |
| 6 | LAN101A | Constitution, Human Rights and Law | 2 | 0 | 0 | 2 | 50 |
| Total | 16 | 1 | 6 | 20 | 550 | ||
| Total number of contact hours per week | 23 | ||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max Marks |
| 1 | MTB102A | Engineering Mathematics-1 | 3 | 1 | 0 | 4 | 100 |
| 2 | CYB104A | Engineering Chemistry and Laboratory | 3 | 0 | 2 | 4 | 100 |
| 3 | MEF104A | Elements of Mechanical Engineering and Work shop Practice | 2 | 0 | 2 | 3 | 100 |
| 4 | EEF105A | Elements of Electrical Engineering and Laboratory | 3 | 0 | 2 | 4 | 100 |
| 5 | CSF106A | Elements of Computer Science and Engineering and Laboratory | 3 | 0 | 2 | 4 | 100 |
| 6 | TSN101A | Professional Communication | 0 | 0 | 2 | 2 | 50 |
| Total | 14 | 1 | 10 | 21 | 550 | ||
| Total number of contact hours per week | 25 | ||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max Marks |
|---|---|---|---|---|---|---|---|
| 1 | MTB102A | Engineering Mathematics-2 | 3 | 1 | 0 | 4 | 100 |
| 2 | PYB102A | Engineering Physics and Laboratory | 3 | 0 | 2 | 4 | 100 |
| 3 | CEF101A | Engineering Mechanics | 3 | 0 | 0 | 3 | 100 |
| 4 | ECF102A | Elements of Electronics Engineering and Laboratory | 3 | 0 | 2 | 4 | 100 |
| 5 | MEF103A | Engineering Drawing | 2 | 0 | 2 | 3 | 100 |
| 6 | LAN101A | Constitution, Human Rights and Law | 2 | 0 | 0 | 2 | 50 |
| Total | 16 | 1 | 6 | 20 | 550 | ||
| Total number of contact hours per week | 23 | ||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max Marks |
|---|---|---|---|---|---|---|---|
| 1 | MTB102A | Engineering Mathematics-2 | 3 | 1 | 0 | 4 | 100 |
| 2 | CYB104A | Engineering Chemistry and Laboratory | 3 | 0 | 2 | 4 | 100 |
| 3 | MEF104A | Elements of Mechanical Engineering and Work shop Practice | 2 | 0 | 2 | 3 | 100 |
| 4 | EEF105A | Elements of Electrical Engineering and Laboratory | 3 | 0 | 2 | 4 | 100 |
| 5 | CSF106A | Elements of Computer Science and Engineering and Laboratory | 3 | 0 | 2 | 4 | 100 |
| 6 | TSN101A | Professional Communication | 0 | 0 | 2 | 2 | 50 |
| Total | 14 | 1 | 10 | 21 | 550 | ||
| Total number of contact hours per week | 23 | ||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max Marks |
|---|---|---|---|---|---|---|---|
| 1 | MTF201A | Engineering Mathematics-3 | 3 | 1 | 0 | 4 | 100 |
| 2 | AIC201A | Basics of Operating Systems | 3 | 0 | 0 | 3 | 100 |
| 3 | AIC202A | Mathematics for Machine Learning I | 3 | 1 | 0 | 4 | 100 |
| 4 | CSD201A | Data Structures Foundation | 3 | 0 | 0 | 3 | 100 |
| 5 | CSD202A | Logic Design | 3 | 1 | 0 | 4 | 100 |
| 6 | AID201A | Principles of Artificial Intelligence | 3 | 0 | 0 | 3 | 100 |
| 7 | BAU201A | Entrepreneurship and Innovation | 3 | 0 | 0 | 3 | 100 |
| 8 | AID202A | Artificial Intelligence Laboratory | 0 | 0 | 2 | 1 | 50 |
| 9 | CSD204A | Python and Data Structures Laboratory | 0 | 0 | 2 | 1 | 50 |
| Total | 21 | 3 | 4 | 26 | 800 | ||
| Total number of contact hours per week | 28 | ||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max Marks |
|---|---|---|---|---|---|---|---|
| 1 | MTF202A | Engineering Mathematics-4 | 3 | 1 | 0 | 4 | 100 |
| 2 | AIC203A | Machine Learning-1 | 3 | 1 | 0 | 4 | 100 |
| 3 | AIC204A | Mathematics for Machine Learning-2 | 3 | 1 | 0 | 4 | 100 |
| 4 | CSD206A | Design and Analysis of Algorithms | 3 | 0 | 0 | 3 | 100 |
| 5 | CSD207A | Programming Paradigms | 3 | 1 | 0 | 4 | 100 |
| 6 | BTN101A | Environmental Studies | 2 | 0 | 0 | 2 | 50 |
| 7 | AIL2O1A | Machine Learning Algorithms Laboratory | 0 | 0 | 2 | 1 | 50 |
| 8 | CSD208A | Programming Paradigms Laboratory | 0 | 0 | 2 | 1 | 50 |
| Total | 17 | 3 | 4 | 23 | 650 | ||
| Total number of contact hours per week | 24 | ||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max Marks |
|---|---|---|---|---|---|---|---|
| 1 | AIC205A | Machine Learning-2 | 3 | 1 | 0 | 4 | 100 |
| 2 | CSC302A | Data Mining | 3 | 1 | 0 | 4 | 100 |
| 3 | CSD203A | Microprocessors and Architecture | 3 | 0 | 0 | 3 | 100 |
| 4 | CSD301A | Computer Networks | 3 | 0 | 0 | 3 | 100 |
| 5 | CSC302A | Database Systems | 3 | 0 | 0 | 3 | 100 |
| 6 | CSD205A | Microprocessors Laboratory | 0 | 0 | 2 | 1 | 50 |
| 7 | CSL301A | Computer Networks Laboratory | 0 | 0 | 2 | 1 | 50 |
| 8 | CSL302A | Database Systems Laboratory | 0 | 0 | 2 | 1 | 50 |
| Total | 15 | 2 | 6 | 20 | 650 | ||
| Total number of contact hours per week | 23 | ||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max Marks |
|---|---|---|---|---|---|---|---|
| 1 | CSC305A | Graph Theory and Optimization | 3 | 0 | 0 | 3 | 100 |
| 2 | CSC309A | Computer Vision | 3 | 1 | 0 | 4 | 100 |
| 3 | CSC310A | Natural Language Processing | 3 | 1 | 0 | 4 | 100 |
| 4 | CSC311A | Deep Learning and Applications | 3 | 0 | 0 | 3 | 100 |
| 5 | CSC312A | Pattern Recognition | 3 | 1 | 0 | 4 | 100 |
| 6 | xxxxxx | Professional Core Elective-1 or Online Course | 3 | 1 | 0 | 4 | 100 |
| 7 | CSS301A | Seminar | 0 | 0 | 2 | 1 | 50 |
| 8 | AIL302A | Natural Language Processing Laboratory | 0 | 0 | 2 | 1 | 50 |
| 9 | AIL303A | Deep Learning and Applications Laboratory | 0 | 0 | 2 | 1 | 50 |
| Total | 18 | 4 | 6 | 25 | 750 | ||
| Total number of contact hours per week | 28 | ||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max Marks |
|---|---|---|---|---|---|---|---|
| 1 | xxxxxx | Professional Core Elective-2 or Online Course | 3 | 1 | 0 | 4 | 100 |
| 2 | xxxxxx | Professional Core Elective-3 or Online Course | 3 | 1 | 0 | 4 | 100 |
| 3 | xxxxxx | Open Elective-1 or Online Course or Innovation Course | 3 | 0 | 0 | 3 | 100 |
| 4 | CSP401A or CSI401A | Project Work-1 or Internship | 0 | 0 | 12 | 6 | 200 |
| Total | 9 | 2 | 12 | 17 | 500 | ||
| Total number of contact hours per week | 23 | ||||||
| Sl. No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max Marks |
|---|---|---|---|---|---|---|---|
| 1 | CSP402A | Project Work-2 or Internship | 0 | 0 | 24 | 12 | 300 |
| Total | 0 | 0 | 24 | 12 | 300 | ||
| Total number of contact hours per week | 24 | ||||||
| Stream | PCE-1 | PCE-2 | PCE-3 |
|---|---|---|---|
| Artificial Intelligence in Healthcare | CSC306A | ISE404A | AIE403A |
| Information Security and Protection | Internet of Things | Artificial Intelligence and Healthcare | |
| Artificial Intelligence and Security | CSE306A | MCC309A | CSE408A |
| Information Security and Protection | Quantum Computing | Computational Intelligence | |
| Big Data Analytics | AIE301A | AIE402A | AIE404A |
| Data Engineering | Time Series Analysis | Graph Analytics for Big Data | |
| Blockchain Technologies | CSE302A | MCC309A | ISE405A |
| Principles and Practices of Cryptography | Quantum Computing | Blockchain Technologies | |
| Applied Mathematics | MTE302A | MTE401A | MTE403A |
| Advanced Mathematics | Optimization Techniques | Advanced Numerical Methods | |
| Data Science and Analytics | CSE411A | CSE421A | CSE431A |
| Data Sciences Foundation | Data Mining | Data Analytics |
Note:
Students are required to select:
One Professional Core Elective Course in the 6th Semester from PCE-1 Group.
Two Professional Core Elective Course in the 7th Semester from PCE-2 and PCE-3 Groups.
Eligibility Criteria
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.
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.
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.
(In all cases of Lateral Entry admissions, the Equivalence Committee decision will be final.)
Structure
Fee Structure
| Total Fee for 1st year | NRI fee in USD |
|---|---|
| 450000 | 7000 |
Intake
AI & ML 90 students.
Career Path
- Data Scientist
- Machine Learning Engineer
- Software Developer
- Robotics Scientist
- Business Intelligence Developer
- AI Research Scientist
- Database Developer
- Deep Learning 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.
Testimonial
“Life changed when I decided to join RUAS and it changed for good! Today I can only tell that it was my luck and fortune to be the part of RUAS”
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