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CERTIFICATE PROGRAMMES

Post Graduate Programme in Artificial Intelligence and Machine Learning

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.

Admissions are now open. Batch starts in Oct 2019. For detailed programme information, download the brochure below.

Pay fee in easy EMIs of INR 13,333 with 0% interest.

Download Brochure Apply Now

  • Programme Highlights
  • Programme Curriculum
  • Programme Objective
  • Learning Outcomes
  • Eligibility Criteria
  • Fee Structure
  • How to Apply
Programme Highlights
  1. A 11-month Post Graduate certificate programme  for working professionals that can be pursued online

  2. Comprehensive and rigourous curriculum covering key concepts and technologies of Artificial Intelligence and Machine Learning

  3. A 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. 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

  5. Extensive digital content including expert lecture videos, and engaging digital learning material

  6. Access to BITS Pilani instructors through online live lectures, a responsive Q&A support and discussion forums

  7. 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.

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

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

Programme Fee: INR 2,25,000 (incl. GST)

(No cost EMI option at INR 13,333 per month available)

Fee Payment Schedule:

Block Amount: INR 25,000 (payable within 7 days of receipt of provisional Admission Offer Letter

Remainder Programme Fee: INR 2,00,000 (payable within 15 days of receipt of Final Admission Offer Letter)

Pay fee in easy EMIs of INR 13,333 with 0% interest. Click here to know more. 

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 his 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 fees of 10% of programme fees (including GST). The deferral request will be approved once the deferral fees is paid. Till this is completed, the participant will be assumed to be continuing in the same cohort. 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 register yourself on 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. You will receive a Provisional Admission Offer Letter within 2 days of receipt of your Application Form.

  3. Upon receiving the Provisional Admission Offer Letter, you will need to submit the following within 7 days using the Online Application Center:

    a. Block amount of: INR 25,000

    b. Scanned copy of Passport size photograph

    c. Scanned copy of self attested Graduation degree certificate and marksheets

    d. Proof of ID (Govt. issued ID such as Driving License, Passport, Aadhar, Voter ID, etc.)

    e. Proof of employment, such as Work Experience Certification from current employer.

  4. Upon receiving the Block Amount and other supporting documents, you will receive a Final Admission Offer Letter. You will need to submit the First Installment (INR 2,00,000) within 15 days of receipt of this letter. You may submit this fee in easy EMIs of INR 13,333 with 0% interest. Click here to know more.

  5. Upon receipt of the remaining Remainder fee, you will receive your BITS Student ID, detailed programme schedule and access to the learning platform.

Programme Objective

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