The eligibility for the e-Masters in AIML is a Bachelor's Degree (4 years program) or a Masters Degree in the relevant discipline (Computer Science/Information Technology/MCA, etc) with at least 55% marks or 5.5/10 CPI. Two (02) years of work experience is a must.

An applicant need not be currently employed to be eligible for admission. Candidates with relevant experience will be preferred. The admissions process will be as rigorous as the admissions process in our graduate programs and may include various phases. The department may apply a short listing criteria to select the initial list of candidates. It will be ensured that only professionals from authentic and reputed companies/organizations will be shortlisted for consideration to the program. An interview will be conducted by the faculty of the Department of Electrical Engineering and Computer Science and Engineering in order to assess the candidates’ suitability for the program. A written admissions test designed to thoroughly evaluate the candidates’ domain knowledge and competence may also be conducted.

An academic year is divided into four quarters; each quarter is of 10 weeks duration. The first 8 weeks will be used for teaching and exams will be held in the 9th/10th week. Each e-Masters student must complete a set of 6 Program Core (PC) modules, which are compulsory and must be completed in order to graduate. Additionally another 6 Program Elective (PE) modules need to be completed from a basket of 9 modules. The full list of modules being offered as well as their content will be available prior to the commencement of each quarter, so as to facilitate the selection of modules.

A sample template for the e-Masters program is as follows:

*PE: Program Elective Modules; PC: Program Core Modules

However the template given above is only an example, since it is not mandatory to take 3 modules per quarter. Indeed, a student may take 0-3 modules per quarter depending on his/her professional workload, and as a result, may take up to 3 years to complete the program.

An admitted student registers for the program once at the beginning of the program, and normally such a registration will be valid for 3 years. At the end of 3 years, a student may apply to extend the program registration if he/she has not completed all requirements of the program. A student is required to register for every module that he/she plans to take in a quarter. Students must register during the registration period for the selected modules subject to credit load constraints.

The module evaluation policies will be announced prior to the commencement of a module in that particular quarter. The evaluation process will be designed to ensure that the high standards of IIT Kanpur processes are maintained. A continuous mode of evaluation will be followed with online assessments like in-video quizzes, online quizzes, assignments, projects, student participation analytics, and mid-quarter examinations. The end-of-quarter examination will be conducted in a proctored mode.

In every registered module, a student will be awarded one of the letter grades A* (10), A (10), B+(9), B (8), C+ (7), C (6), D+ (5), D (4), E (0), and F(0) indicating his/her overall performance in the module. A student earns 5 credits for each module where he/she is awarded at least an D grade. A student getting an E or a F grade in a module must either repeat it or substitute it by another module as suggested by the Department. A student getting a D or D+ grade in a module may be allowed to repeat it.

A student should have earned 60 credits, earned a CPI of 6, paid all dues to the Institute, and should have no pending case of indiscipline against him/her to graduate.

In addition to the courses, candidates are required to do one/ two weeks of on-campus practical training involving hands-on lab sessions, testbed demonstrations, and lab visits in the state-of-the-art/cutting edge areas of Machine Learning/AI for Communications.

The following are the tentative modules of study that will be offered in this Program:

Program Core Modules

• Data Analytics and Data Structures
• Introduction of Linear Algebra
• Introduction to Machine Learning
• Basics of Optimization
• Deep Learning Fundamentals
• Probability and Statistics for Machine Learning

Program Elective Modules

• ML for Audio Processing
• Computer Vision
• Natural Language Processing
• ML with Large Datasets
• AI in IoT
• AI in Healthcare
• Industrial AI / Automation
• Reinforcement Learning
• Capstone Project

The list is tentative and new modules will be continuously added while obsolete ones will be removed over time.

The academic calendar, student forms, Students Handbook and all other relevant information are available on the DOAA website, IIT Kanpur at ‘eMasters Students’ tab. https://www.iitk.ac.in/doaa/