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.


Programme Highlights

  1. Industry-relevant curriculum, delivered online or on-site lectures.
  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

The programme offers a degree of customisation to address the specific L&D needs of your organisations.


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

Eligibility Criteria

Minimum eligibility to apply:

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

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