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Post Graduate Programme in Artificial Intelligence and Machine Learning

Build the Future.

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According to the recent LinkedIn's Emerging Jobs report, Artificial Intelligence had a significant presence in the top emerging jobs in 2018. AI was one of the fastest growing skills on LinkedIn and recorded a 190% global increase in demand between 2015-2017.

Furthermore, a report from The World Economic Forum predicts that the growth of artificial intelligence could create 58 million net new jobs in the next few years.

The BITS Pilani Post Graduate Programme in Artificial Intelligence and Machine Learning is an 11-month programme designed for technology professionals, and can be pursued without taking a career break. The programme focuses on building skills that will enable professionals for a rewarding career in this high-growth domain.

The programme is designed for technology professionals who wish to advance their career as a specialist in the field of Artificial Intelligence and Machine Learning. Professionals who wish to transition to roles such as Data Scientist, Machine Learning Engineer, AI Product Manager, Data Engineer, and Applied ML Scientist should consider applying to this programme.

Admission Enquiry

Please download the programme brochure for Fee details, Curriculum, and Application instructions by sharing your details.

  • Programme Highlights
  • Programme Curriculum
  • Programme Objectives
  • Learning Outcomes
  • Eligibility
  • Fee Structure
  • How To Apply

Programme Highlights

  • 11-month Post Graduate certificate programme for working professionals that can be pursued online
  • Comprehensive and rigourous curriculum covering key concepts and technologies of Artificial Intelligence and Machine Learning
  • 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
  • Two Campus Immersion modules of 2-days each at the Hyderabad Campus of BITS Pilani, during which participants will visit the Campus to interact with their peers and learn together from BITS faculty
  • Extensive digital content including expert lecture videos, and engaging digital learning material
  • Access to BITS Pilani instructors through online live lectures, a responsive Q&A support and discussion forums
  • Participants who successfully complete the programme will become members of an elite & global community of BITS Pilani Alumni
  • Option to pay through No-cost EMI option with 0% interest available

Programme Curriculum

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

Course 1: Regression

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.

Topics covered
  • 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
Course 2: Feature Engineering

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.

Topics covered
  • Overview of Feature Engineering
  • Data Preprocessing
  • Dimensionality Reduction
  • Visualization
Course 3: Classification

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.

Topics Covered
  • Overview of the Classification Module
  • Nearest-neighbour Meathods
  • 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
Course 4: Unsupervised Learning & Association Rule Mining

The course on Unsupervised Learning & Association Rule Mining focuses in 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.

Topics Covered
  • 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
Course 5: Text Mining

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.

Topics Covered
  • 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
Course 6: Deep Learning and ANN

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.

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

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.

Refresher Course
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

Programme Objectives

The programme is designed to:
  • Enable technology professionals to be industry-ready in rapidly growing domain of AI & ML
  • Produce professionals with strong algorithmic perspective of AI & ML
  • Provide a comprehensive understanding of the data science pipeline
  • Provide deeper understanding of AI & ML techniques to enhance informed decision making
  • Build skills that are necessary to solve real-world business problems using AI & ML

Learning Outcomes

Upon completion of this programme, participants will be able to:
  • Decide whether AI & ML techniques are applicable for a given business problem and articulate its benefits thereof
  • Formulate business problems as AI & ML problem
  • Collect data and apply pre-processing techniques
  • Apply Supervised Learning, Unsupervised learning, Deep Learning, Visualization techniques
  • Identify appropriate techniques to solve the formulated AI & ML problem
  • Implement and compare the relevant algorithms using Python
  • Interpret and present the predicted model

Eligibility

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

For details on Fee Structure, please click here to download the programme brochure.

How To Apply

For instructions on how to apply, please click here to download the programme brochure

Admissions Open. Batch starts Oct 2019