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M.Tech. Programmes

DEGREE PROGRAMMES M.Tech. Artificial Intelligence & Machine Learning

M.Tech. Artificial Intelligence & Machine Learning

With a surge of job opportunities in the fields of Artificial intelligence and Machine Learning, the world is indeed standing on the threshold of massive transformation.

According to the World Economic Forum's Future of Jobs Report, 85 million jobs will be replaced by machines with AI by 2025. While that might make you uneasy, the same report states that 97 million new jobs will be created by 2025 due to AI. Are you prepared? 

Prepare for a career with infinite possibilities in AI and ML with India’s most comprehensive and world-class M.Tech. Artificial Intelligence and Machine Learning programme without taking any career break.

This four-semester programme covers a wide variety of skills and knowledge areas, and enables IT professionals and Software developers to build a skill set that enables career elevation in some of the most sought-after job roles such as ML Engineers and AI Scientists, etc.

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  • Programme Highlights
  • UGC Approval
  • Programme Curriculum
  • Learning Methodology
  • Eligibility Criteria
  • Fee Structure
  • How to Apply
Programme Highlights

  • The programme is of four semesters, with online classes conducted mostly on weekends or after business hours. You can pursue the programme without any career break.

  • Offers the most comprehensive AI & ML Curriculum for working professionals.

  • The programme has an unmatched range & depth, and covers the widest variety of skill & knowledge areas required to develop advanced AI solutions.

  • Meant for IT professionals and Software developers aiming to become expert Machine Learning Engineers & AI Scientists.

  • The programme offers a set of core courses and elective courses, allowing students to gain expertise in Advanced Deep learning, Computational Learning theory, Speech Processing, Natural Language Processing, etc..

  • The programme makes use of Tools and Technologies. These include Tensorflow for Deep Learning and various Python libraries for data processing, machine learning, OpenCV for computer vision, NLTK for NLP etc.

  • The Dissertation (Project Work) in the final semester enables students to apply concepts and techniques learned during the programme.

  • The programme uses a Continuous Evaluation System that assesses the learners over convenient and regular intervals. Such a system provides timely and frequent feedback and helps busy working professionals stay on course with the programme.

  • The education delivery methodology is a blend of classroom and experiential learning. Experiential learning consists of Virtual lab exercises, assignments, case studies and work-integrated activities.

  • Participants who successfully complete the programme will become members of an elite & global community of BITS Pilani Alumni

  • Option to submit fee using easy-EMI with 0% interest and 0 down payment .

UGC Approval

BITS Pilani is an Institution of Eminence under UGC (Institution of Eminence Deemed to be Universities) Regulations, 2017. The Work Integrated Learning Programmes (WILP) of BITS Pilani constitutes a unique set of educational offerings for working professionals. WILP are an extension of programmes offered at the BITS Pilani Campuses and are comparable to our regular programmes both in terms of unit/credit requirements as well as academic rigour. In addition, it capitalises and further builds on practical experience of students through high degree of integration, which results not only in upgradation of knowledge, but also in up skilling, and productivity increase. The programme may lead to award of degree, diploma, and certificate in science, technology/engineering, management, and humanities and social sciences. On the recommendation of the Empowered Expert Committee, UGC in its 548th Meeting held on 09.09.2020 has approved the continued offering of BITS Pilani’s Work Integrated Learning programmes.

Programme Curriculum

First semester

  • Mathematical Foundations for Machine Learning

  • Introduction to Statistical Methods

  • Artificial and Computational Intelligence

  • Machine Learning

Second Semester

  • Deep Neural Networks

  • Deep Reinforcement Learning

  • Elective 1

  • Elective 2

Third Semester

  • Elective 3

  • Elective 4

  • Elective 5

  • Elective 6

Fourth Semester

  • Dissertation

Pool of Electives for : Deep Learning Specialization

  • Advanced Deep learning #

  • Graph Neural Networks

  • Distributed Machine Learning

  • Computational Learning Theory

  • ML System Optimization

  • Fair, Accountable, Transparent Machine Learning

note:3 courses are required including the course marked in #

Pool of Electives for : NLP Specialization

  • NLP Applications

  • Speech Processing

  • Conversational AIx

  • Social Media Analytics

  • Natural Language Processing #

  • Information Retrieval

note:3 courses are required including the course marked in #

General Pool of Electives

  • MLOps

  • Design of Algorithms

  • Computer Vision

  • Probabilistic Graphical Models

  • Audio Analytics

  • AI and ML for Robotics

  • Data Management for Machine Learning

  • Video Analytics

  • Automated Reasoning

  • Advanced Data Mining

  • AI and ML techniques for Cyber Security

  • Metaheuristics for Optimization

Note: Choice of Electives is made available to enrolled students at the beginning of each semester. Students' choice will be taken as one of the factors while deciding on the Electives offered. However, Electives finally offered will be at the discretion of the Institute

Learning Methodology
Online lectures

ATTEND ONLINE LECTURES OVER WEEKENDS

  • Lectures are conducted live via online classes. These lectures can be attended via the internet using a computer from any location. These online classrooms offer similar levels of interactivity as regular classrooms at the BITS Pilani campus.

  • The class schedule is announced within 1 week of completion of the admission process.

  • The online lectures are conducted usually over weekends for a total of 7-8 hours per week. If you miss a lecture, you can also access the recorded lecture on the internet.

Digital Learning

DIGITAL LEARNING

Learners can access engaging learning material which includes recorded lectures from BITS Pilani faculty members, course handouts and recorded lab content where applicable.

experimental Learning

EXPERIENTIAL LEARNING

The programme emphasises on Experiential Learning that allows learners to apply concepts learnt in the classroom in simulated, and real work situations. This is achieved through:

  • Simulation Tools, Platforms & Environments: Some or all of the following would be utilised across the programme. Tensorflow for Deep Learning and various Python libraries for data processing, machine learning, OpenCV for computer vision, NLTK for NLP etc.

Case Studies and Assignments: Carefully chosen real-world cases & assignments are both discussed and used as problem-solving exercises during the programme.

Dissertation/ Project Work: The fourth semester offers an opportunity for learners to apply their knowledge gained during the programme to a real-world like complex project. The learner is expected to demonstrate understanding of vital principles learnt across semesters and their ability to successfully apply these concepts.

Continour Assessment

CONTINUOUS ASSESSMENT

Continuous Assessment includes graded Assignments/ Quizzes, Mid-semester exam, and Comprehensive Exam.

Eligibility Criteria

  • Employed professionals holding B.E. / B.Tech. with at least 60% aggregate marks and minimum 18 months relevant work experience within HCL Technologies, are eligible to apply.

  • Employed professionals holding MCA / M.Sc. or equivalent with at least 60% aggregate marks with university level mathematics / statistics as mandatory subjects and minimum 18 months relevant work experience within HCL Technologies, are also eligible to apply.

  • Working knowledge of Computing and programming is required.

  • The above are only the minimum criteria to apply. The final decision to offer admission to an applicant rests with BITS Pilani which will be made based on an overall review of your application information.

  • It is strongly advised to refer and check HCL's policy details and other Eligibility Criteria of the programme before applying, as all fees are non-refundable.

Fee Structure

  • The following fees schedule is applicable for candidates seeking new admission during the academic year 2023-2024.

    Application Fees (one time) : INR 1,500

    Admission Fees (one time) : INR 16,500

    Semester Fees (per semester) : INR 68,500

  • The one-time Application Fee is to be paid at the time of submitting the Application Form through the Online Application Centre.

  • Admission Fee (one-time) and Semester Fee (for the First Semester) are to be paid together once admission is offered to the candidate. Thus, a candidate who has been offered admission will have to pay Rs. 85,000/-. You may choose to make the payment using Netbanking/ Debit Card/ Credit Card through the Online Application Centre.Fee payment by easy-EMIs with 0% interest. Click here to learn more.

  • Semester Fee for subsequent semesters will only be payable later, i.e. at the beginning of those respective semesters.

  • Any candidate who desires to discontinue from the programme after confirmation of admission & registration for the courses specified in the admit offer letter will forfeit the total amount of fees paid.

  • For the examination centre at Dubai, in addition to the semester fees, for each semester there will be an examination centre fees of 1000 UAE Dirhams or equivalent per semester out of which 500 UAE Dirhams is to be paid at the time of appearing in Mid-semester examinations at Dubai Centre for that semester and the remaining 500 UAE Dirhams is to be paid at the time of appearing in comprehensive examinations at Dubai centre for that semester.

  • All the above fees are non-refundable.

How to Apply

  • Click here to visit the Online Application Center. Create your login at the Online Application Center by entering your official HCL Email ID only and create a password of your choice. Once your login has been created, you can anytime access the Online Application Center using your official email ID and password.

  • Begin by clicking on Step 1 - ‘Fill/ Edit and Submit Application Form’. This will enable you to select the programme of your choice. After you have chosen your programme, you will be asked to fill your details in an online form. You must fill all details and press ‘Submit’ button given at the bottom of the form.

  • Now, click on 'Pay Application Fee’ to pay INR 1,500/- using Netbanking/ Debit Card/ Credit Card

  • Finally, click on 'Upload & Submit All Required Documents’. This will allow you to upload one-by-one all the mandatory supporting documents such academic certificates and transcripts, photograph, etc. and complete the application process. Acceptable file formats for uploading these documents are .DOC, .DOCX, .PDF, .ZIP and .JPEG

  • Upon receipt of your Application Form and all other enclosures, the Admissions Cell will scrutinise them for completeness, accuracy and eligibility.

  • Admission Cell will intimate selected candidates by email within two weeks of submission of application with all supporting documents. The selection status can also be checked by logging in to the Online Application Centre.