CMR University Bengaluru rated amongst "Perfect Workplaces For Women in 2022" by Artificial Intelligence
Registration and Certification, U.K.

CMR University Bengaluru receives "5-star ratings" by Artificial Intelligence Registration and Certification, U.K., and
is now one of the "Best Places to Work" in 2022.

CMR University Bengaluru secures 40th position, in the 2021 Global Impact Rankings by R World Institutional Ranking,
for contributing towards Sustainable Development Goals of the Earth.

M.Tech. CSE (Artificial Intelligence) Programme

Programme Overview

  • Artificial Intelligence, also known as Machine Intelligence, is the intelligence demonstrated by machines, as compared to the natural intelligence demonstrated by humans. Artificial Intelligence focuses primarily on the development and understanding of intelligent computational processes for the benefit of both creating artificial beings and improving the understanding of human intelligence.
  • In this Artificial Intelligence course, students can understand the related functional domains such as computer vision, natural language processing, software analytics and robotics.

Career Opportunities

  • Artificial intelligence Engineer
  • Data Science Analyst
  • Data Science Engineering
  • Machine learning Engineer
  • NLP Engineering

Programme USPs

  • All the programmes are developed on a knowledge skill and ability model (KSA).
  • The Curriculum design is properly reviewed by CMR University/ institutes of national and international eminence.
  • The curriculum is designed on Outcome Based Education (OBE), aligned with industry requirements and in sync with global certifications
  • The programme delivery is through faculty and experts with good academic and industrial experience with a practical oriented approach blended with industry immersion activities and project work duly vetted by industry experts.
  • Unique delivery model. The programmes are delivered through a unique technology enabled blended delivery model, with ‘The New 3E’s of Education’- Enable, Engage and Empower. An experimental and exploratory approach towards teaching and learning is followed with up to 60% of it being practice based.
  • The learning experience is enriched through in house developed Learning Management System (LMS) where students and faculty can access state of art designed content and other artefacts of the learning system like quizzes, assignments, discussion forum and News feed.
  • 360 degree placement assistance provided by CMRU placement team through well designed research based Pre-Placement training programmes, technical tests and Mock interviews by industry experts All the training programmes are designed after taking the inputs through our uniquely designed placement quotient test. Our students are hired by many well established and reputed corporate, demonstrating the success of our model.

Programme Outcomes

  • Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and Computer science and engineering specialization to solve the complex engineering problems.
  • Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using mathematics, basic sciences, and computer science and engineering especially in the field of Artificial Intelligence.
  • Design/Development of Solutions: Design solutions for complex engineering problems using appropriate knowledge of Artificial Intelligence for the health sector, finance sector, manufacturing and Supply chain sectors.
  • Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information using machine learning algorithms to provide valid conclusions.
  • Modern Tool Usage: Create, select and apply appropriate techniques, resources and IT tools (Python Programming, R Studio, Tensorflow, Keras, and the other platforms providing AI solutions including AWS) which helps in building models using Artificial Intelligence.

Programme Duration

2 Years ( 4 Semesters )

Programme Type

Full Time

Eligibility Criteria

Candidate who has Passed in Bachelor’s Degree (B.E. or B.Tech.) or equivalent in the relevant field and obtained at least 50% marks in the aggregate of all 4 years of degree examinations. (45% in case of candidate belonging to reserved category). Candidate must also qualify in one of the following entrance exams: – GATE / PGCET.


Programme Structure


I Semester

II Semester

III Semester

IV Semester

Assessment and Evaluation

The University follows Choice Based Credit System (CBCS), which provides choice for students to select from the prescribed set of courses and earn credits. Students are awarded grades based on their performance for each courses in a semester and Semester Grade Point Average (SGPA), which is a measure of academic performance of a student in a semester. Cumulative Grade Point Average (CGPA) is used as a measure of overall cumulative performance of a student over all semesters. However, the CGPA is invariably calculated from second semester onwards to facilitate students to know their academic progress.

Every Programme has a prescribed Curriculum or the Scheme of Teaching and Evaluation. It prescribes all the courses/ laboratory/ other requirements for the degree and sets out the nominal sequence semester wise. Curriculum also includes SWAYAM and Massive Open Online Courses (MOOCs), offered by premier institutions. A student desirous of additional exposure to a course, without the rigors of obtaining a good grade, ‘audits’ a course that helps him to have an edge over others in placements.

The evaluation system to assess the student is comprehensive and continuous during the entire period of Semester, by the faculty who is teaching the course. Continuous Internal Evaluation (CIE) and Semester End Examination (SEE) constitute the major evaluations prescribed for each course, with only those students maintaining a minimum standard in CIE are permitted to appear in SEE of the course. CIE and SEE to carry 50% weightage each, to enable the course to be evaluated for a total of 100 marks, irrespective of its credits.

Before the start of the Academic session of each semester, a faculty may choose for his course Internal Assessment Test and a minimum of two of the following assessment methods with suitable weightage for each: Assignments (Individual and/or Group), Seminars, Quizzes, Group Discussions, Case studies/Case lets, Practical orientation on Design Thinking, Creativity & Innovation, Participatory & Industry-integrated learning, Practical activities / problem solving exercises, Class presentations, Analysis of Industry/Technical/Business Reports, Reports on Guest Lectures / Webinars / Industrial Visits, Industrial / Social / Rural projects, Participation in Seminars/ Academic Events/Symposia, etc. or any other academic activity.

The Semester End Examination for all the courses for which students registered during the semester shall be conducted at the end of each semester. Some of the courses, where the student performance is evaluated through CIE, may not have SEE.

The makeup examination facility shall be available to those students who have appeared and failed in the SEE in one or more courses in a semester.



B.Tech Bagalur Campus - E257
B.Arch Bagalur Campus - E245


M.Tech Bagalur Campus - T987
MBA, City Campus - B150
MBA, Bagalur Campus - B395
MCA, Bagalur Campus - C521


B.Tech Programs, Bagalur Campus: E187
B.Arch Program, Bagalur Campus : E187(A)