Post Graduate Programme in Artificial Intelligence and Machine Learning

The Annual list of Indeed’s "25 best jobs of 2019" named the job of a Machine Learning Engineer as No. 1, citing a 344% increase in job postings in the last few years. The future will be built on Artificial Intelligence and Machine Learning. Are you ready to be a part of it?

The BITS Pilani 11-month online PG Programme in AI & ML is designed to help working professionals like you develop an understanding of AI & ML and its various building blocks.

Admissions Open, Last Date to Apply: Sep 14, 2020. For detailed programme information, download the brochure below.


11 months

Easy EMI Option With

0% Interest

Last Date To Apply

Sep 14, 2020

Programme Fee

INR 2,45,000

Programme Highlights

  1. 11-month Post Graduate certificate programme for working professionals that can be pursued online
  2. Comprehensive and rigorous curriculum covering key concepts and technologies of Artificial Intelligence and Machine Learning
  3. An 8-week Capstone project where you will work towards solving a Data Science related business problem under the mentorship of BITS Pilani faculty members and senior industry practitioners
  4. Access to BITS Pilani instructors through online live lectures, Q&A support and discussion forums
  5. Participants who successfully complete the programme will become members of an elite & global community of BITS Pilani Alumni

Programme Curriculum

The 11-month online Post Graduate Programme in Artificial Intelligence and Machine Learning consists of 6 Courses and a Capstone Project


Regression is a widely used statistical learning method, and this course will enable participants to have a deeper understanding of regression models both from theoretical and implementation perspective.

The course covers concepts such as lasso regression, ridge regression and the interpretability of the predicted models.

  • Overview of certificate programme in ML & AI
  • Introduction to Regression
  • Mathematics Foundations
  • Model Building using Least squares
  • Model Accuracy & Selection
  • Overfitting
  • Interpretability of regression models

Feature Engineering is an important step to develop and improve performance of Machine Learning models. In this course, students will learn different data wrangling techniques that help transforming the raw data to an appropriate form on which learning algorithms can be applied.

This course enables students to identify and implement appropriate feature extraction and pre-processing techniques. The Visualization techniques will also be taught in this course.

  • Overview of Feature Engineering
  • Data Preprocessing
  • Dimensionality Reduction
  • Visualization

The course on Classification lays down a strong foundation on the algorithmic perspective of popular classification algorithms - k-NN, Naïve Bayes, Decision Tree, Logistic Regression and SVM. The implementation details of these models, along with tuning of parameters will be illustrated. The course also covers concepts such as ensemble methods like bagging, boosting, Random Forest, and interpretability of the predicted models.

  • Overview of the Classification Module
  • Nearest-neighbour Methods
  • Naïve Bayes Classifier
  • Logistic Regression
  • Decision Tree
  • Optimization Foundations for Support Vector Machines
  • Support Vector Machines
  • Support Vector Machines in overlapping class distributions & Kernels
  • Ensemble Methods

The course on Unsupervised Learning & Association Rule Mining focuses on finding natural groups or clusters that are present in the data. The course will cover lustering algorithms like K-means, Hierarchical & DBSCAN algorithms, Hidden Markov Models for time series prediction, and market basket analysis to generate the interesting rules from a transactional database.

  • Introduction to Unsupervised Learning, Clustering
  • K-Means Algorithm, K-Means – Variations, Detecting Outliers
  • Math Fundamentals for EM Algorithm, EM Algorithm, Clustering for Customer Segmentation
  • Hierarchical Clustering
  • Density Based Clustering, Clustering for Anomaly Detection
  • Assessing Quality of Clustering, Significance of Clustering - Interpreting/ summarizing Clusters by businesses
  • Association Rule Mining, Apriori Algorithm
  • Time series Prediction and Markov Process, Hidden Markov Model
Text Mining (5 weeks)

Text mining is the process of deriving high-quality information from text and this is the fifth course of the program. This course aims to equip students with adequate knowledge in extracting the relevant text data and skills to identify patterns therein. This course covers topics like converting documents to vectors, parts of speech tagging, topic modelling, sentiment analysis and recommender systems.

  • Document vectorization and Parts of Speech Tagging
  • Introduction to Part of speech tagging, Part of speech tagging using HMM-1, Implementing POS Tagging in Python
  • Topic modelling using LDA
  • Introduction to Sentiment Analysis
  • Recommender Systems
Deep Learning and ANN (6 weeks)

Deep learning is an evolving subfield of Machine Learning and this course starts with traditional Neural Networks followed by sequential networks, Convolution Networks, Autoencoders and Generative deep learning models. The implementation details of these deep learning models along with tuning of the parameters will be illustrated in this course.

  • Artificial Neural Network
  • Sequence Modeling in Neural Network
  • Deep Learning
  • Convolution Networks with Deep Learning
  • Autoencoders with Deep Learning
  • Generative deep learning models

During the 8-week Capstone Project, participants will work in teams to design and solve a real-world business problem encompassing data science pipeline using AI&ML techniques under the mentorship of BITS Pilani faculty members and senior Industry practitioners.


In addition to the Curriculum above, participants will have the option of taking an optional course on Python at the beginning of the Programme. This will allow participants to revisit essential concepts that will help in all other courses during the programme. Topics covered include Introduction to Python programming and installation, Data Types, Program constructs, Numpy, Pandas, Matplotlib, and Debugging Python programs

For detailed programme curriculum, download the brochure.

Eligibility Criteria

Employed professionals holding BE/ B.Tech. or equivalent, and working in relevant fields are eligible to apply. Candidates holding M.Sc. in Mathematics or Statistics, and working in relevant roles are also eligible to apply to this programme. A working knowledge of languages such as Python is recommended

Fee Structure

The following fees schedule is applicable for the Post Graduate Programme in AI & Machine Learning.

Programme Fees : INR 2,45,000 (including GST)

Fee Payment Schedule:

  • Block Amount: INR 25,000 (payable within 7 days of receipt of provisional Admission Offer Letter
  • Remainder Programme Fee: INR 2,20,000 (payable within 15 days of receipt of Final Admission Offer Letter)

Refund: Participants may cancel their admission within the first 14 days from the start of the cohort, i.e. Programme Start date (launch of Course 1). He/ She will be eligible to get a full refund of programme fee paid, minus the bank processing charges, and applicable taxes (the taxes won’t be refunded). Refund will be processed within a maximum of 45 working days. The participant will be required to fill in a refund form that will be made available by the Admission Cell.

Deferral: If a participant is facing severe issues in dedicating time to the course, we provide the opportunity for the participant defer to another batch. Participants can request for deferral ONLY ONCE and to the next immediate scheduled cohort of the same programme. Participants will be required to pay a deferral fee of 10% of programme fees (including GST). The deferral request will be approved once the deferral fee is paid. Till this is completed, the participant will be assumed to be continuing in the same cohort. The Participant will start learning on the new cohort from the point of leaving the deferred cohort. If, however, the deferral request is raised before the issue of BITS Student ID, the 10% deferral fees will not be charged, and participant will be deferred to the next scheduled cohort. However, in case there is any fee differential between his current cohort and the cohort he/she has deferred to, the participant will have to pay the differential amount.

How to Apply

  1. Click here to visit the BITS Pilani Online Application Center. Create your login at the Application Center by entering your unique Email id and create a password of your choice. Once your login has been created, you can anytime access the Online Application Center using your email id and password.
  2. Once you have logged in, you will see a screen showing 4 essential steps to be completed to apply for the programme of your choice.
  3. 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 the details and press ‘Submit’ button given at the bottom of the form.
  4. Take the next step by clicking on Step 2 - 'Download Application PDF Copy’. This will download a pdf copy of the application form on your computer.
  5. Now, click on Step 3 - 'Pay Application Fee’ to pay INR 1,500/- using Netbanking/ Debit Card/ Credit Card.
  6. Take a printout of the downloaded Application Form and note down the Application Form Number displayed on the top-right corner of the first page. This Application Form Number should be referred in all future correspondence with BITS Pilani.
  7. In the printout of the downloaded Application Form, you will notice on page no. 3 a section called the Employer Consent Form. Complete the Employer Consent Form. This form needs to be signed and stamped by your organisation’s HR or any other authorised signatory of the company.
  8. Further on page no. 4 of the printed Application Form is a section called the Mentor Consent Form. The Mentor Consent Form needs to be signed by the Mentor. Click here to know who could be a Mentor.
  9. Further on page no. 5 of the downloaded Application Form, is a Checklist of Enclosures/ Attachments.
  10. Make photocopies of the documents mentioned in this Checklist.
  11. Applicants are required to self-attest all academic mark sheets and certificates.
  12. Finally, click on Step 4 - 'Upload & Submit All Required Documents’. This will allow you to upload one-by-one the printed Application Form, Mentor Consent Form, Employer Consent Form, and all mandatory supporting documents and complete the application process. Acceptable file formats for uploading these documents are DOC, DOCX, PDF, ZIP and JPEG.
  13. Upon receipt of your Application Form and all other enclosures, the Admissions Cell will scrutinise them for completeness, accuracy and eligibility.
  14. 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.

Batch Profile

Student Speak

Industry Endorsements


This programme is designed for working professionals. At the time of submitting the application, candidates must be employed in another organization. Professionals who are owners of a registered business are also eligible to apply.
For detailed information, including academic background, work experience, etc. refer to the programme eligibility criteria.

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 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.

Upon successful completion of the programme, participants will receive a degree certificate from BITS Pilani.

Each semester has a Mid-semester Exam and a Comprehensive Exam, which are conducted over weekends. Participants will need to appear in-person to take these exams at exams centers in the following locations:
India Centers: Bangalore, Chennai, Coimbatore, Delhi, Goa, Hyderabad, Kolkata, Mumbai, Pilani, Pune
International Centers: Dubai
In case participants are unable to take an exam due to work-related commitments, there is also a provision of appearing for Make-up Exams.

The programme provides high degree of interactivity between participants and the faculty members and programme instructors. Q&A sessions during the live online lectures allow participants to pose questions to the faculty members, and seek guidance through voice and chat. Further interaction with faculty members and peers is enabled through the e-learning portal, by using discussion forums and message boards. 

The programme features high usage of experiential learn components such as Simulations, Virtual Labs, and Remote Labs, in order to mimic the on-campus experience.
Participants will be given access to portals, that will allow them to access both cloud-based labs, as well as Campus-based physical labs. Using leading industry-recognised Software tools, Programming languages, and Simulation software, participants will be able to perform experiments and run simulations to advance their knowledge

Learning Continuity Assurance (LCA) is a unique initiative of BITS Pilani, that further empowers programme participants should there be a disruption in their pursuit of the programme. In case a participant needs to take a break in the middle of the programme, the LCA will help them preserve the money invested. Such participants will be able to rejoin the programme at a later date, and continue from where they left off.
For detailed information, please refer to the programme brochure.

Candidates applying to the programmes must choose a Mentor, who will monitor the academic progress of the candidate, and act as an advisor & coach for successful completion of the programme. Candidates should ideally choose the immediate supervisor or another senior person from the same organisation. In case a suitable mentor is not available in the same organisation, a candidate could approach a senior person in another organisation who has the required qualifications. For detailed information, please refer to the programme brochure.
Kindly note that BITS Pilani does not assign Mentors to programme participants.

The programme is designed for working professionals, and participants are not required to travel to a BITS campus. However, certain programme offer Campus Immersion Modules such as workshops or seminars, which are highly recommended but not mandatory.