IIM Kashipur - PG Course in AI and Machine Learning

“Post Graduate Certificate Programme in AI and Machine Learning from Indian Institute of Management, Kashipur (IIM Kashipur).”

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Course Overview

This program will equip the participants with a clear understanding of the major concepts of Artificial Intelligence and Machine Learning. The participants can then use these skills and computational tools to generate valuable insights and become experts in their domain. The sessions will be exercise-based so that participants can apply the concepts that they have learned.

The philosophy of this program is to introduce participants to the world of AI and machine learning. This program is intended to equip the participants with clear understanding of some of the AI and machine learning concepts frequently used in the industry to generate insights from data, and to enable the participants to be able to use the freely available computational tools to use these concepts on their own.

This program will follow a structure of simultaneously discussing the machine learning concepts, demonstrating how to implement these concepts using hands on tutorials, and identifying the key business/managerial insights that can be generated from these exercises. This triple-layered structure will help the participants to be able to relate to the discussions in a more effective manner.

TOP AI STATISTICS & TRENDS

  • AI-powered voice assistants will become an $8 billion market by 2023.
  • By 2025, the global AI market is expected to grow to almost $60 billion.
  • Global GDP will grow by $15.7 trillion by 2030 thanks to AI.
  • AI is expected to increase business productivity by 40%.
  • Already 77% of the devices we use feature AI in one form or another.

Key Benefits

  • Alumni status & certification by IIM Kashipur
  • Industry relevant curriculum
  • Capstone project
  • Eminent faculty panel
  • Hands on exposure on tools
  • 5 days campus visit at IIM Kashipur
  • Experiential learning in AI & Machine Learning
  • 30 hrs. Tableau masterclass by Nulearn

Mode of Delivery

This Program is delivered through D2D Sessions. D2D referes to Direct to Device that enable students to attend sessions from anywhere on their own systems as per LIVE schedule of session.

Eligibility

Education

Graduates (10+2+3) or Diploma Holders (only 10+2+3) from a recognized university in any discipline.


Experience Minimum

1 year of minimum work experience is required to apply

Selection Process

Profiles will be assesed by the  faculty of IIM Kashipur, on the basis of Academics, work experience and Statement of Purpose.

Certification

"Post Graduate Certificate in Artificial Intelligence and Machine Learning" will be awarded by IIM Kashipur to students on successful completion of the programme.

Alumni Status/ Placement

On successfull completion; IIM Kashipur  will award Alumni status to students

Course Content

Module: 1- Introduction to AI and Machine Learning

  • In this module, participants will master the basics of the widely used open-source language R and Python programming. Participants will be able to utilize the various packages available in R and Python programming for visualization, reporting, data manipulation, and statistical analysis. Students can import data sets, transform, and manipulate those datasets for various analytical purposes. This module also briefs about artificial intelligence, business analytics, and machine learning.

AI Introduction

  • Introduction to AI
  • Goals of Artificial Intelligence
  • Evolution of Artificial Intelligence

Business Analytics and Machine Learning

  • Overview of Business Analytics
  • Foundations of Business Analytics and Data-Driven Decision Making
  • Basics of Machine Learning

Introduction to R

  • Introduction to R Programming
  • Getting Started with R
  • Loading and Handling Data in R
  • Exploring Data in R
  • Operators and Expressions
  • Control Structures
  • Loops
  • Functions
  • R Packages

Introduction to Python

  • Basics of Python Programming
  • Operators and Expressions
  • Decision Statements
  • Loop Control Statements
  • Functions & Python Packages
  • Working with Files
  • Object Oriented Concepts

Module: 2- Statistical Methods in Business Analytics

  • In this module, participants will be introduced to fundamentals of statistical methods and its applications in business practices. Participants will also learn about steps of conducting Data Analysis, ways of summarizing data using charts and graphs, and drawing inferences from the data.

Probability and Random Variables

  • Random Experiment, Rules of Probability
  • Conditional Probability, Statistical Independence
  • Joint Probability, Marginal Probability
  • Random Variables, Expectation and Variance
  • Probability Distribution of Discrete and Continuous Variables

Descriptive Analytics

  • Measures of Central Tendency, Dispersion and Shape
  • Correlation and Covariance
  • Outliers and Missing Data

Inferential Analytics

  • Sampling Methods
  • Statistical Plots and Applications: Histogram, Bar Plot, Pie Chart, Box Plot, Density Plot, Scatter Plot etc.
  • Creating Awesome Plots using R/Python

Data Driven Decision-Making

  • Problem Solving in Simulated/Real Business Situations

Module: 3- AI -Supervised Machine Learning & Applications - Part I

  • This module prepares participants to develop the necessary understanding and skills to use data and supervised machine learning techniques for causal inference. The module begins with an understanding of causality, and then move towards learning how to establish causality using regression models. Participants will also understand the basics of time series forecasting.

Causal Inference

  • A Brief History of Causal Inference
  • Causal Machine Learning
  • Directed Acyclic Graph (DAG)

Building Statistical Models

  • The Art and Science of Statistical Modelling
  • Statistical Modelling from a Business Perspective

Dimensionality Reduction Techniques

  • Principal Component Analysis

Regression Techniques

  • Motivation for Linear Regression
  • Ordinary Least Squares
  • Inference
  • Dummy Variables
  • Functional Forms
  • Advanced Techniques
  • Linear Probability Model

Time Series and Forecasting Techniques

  • Trends and Seasonality
  • Stationarity
  • Error Correction Models
  • One-step-ahead Forecast
  • Multiple-step-ahead Forecast

Module: 4- AI - Supervised Machine Learning & Applications - Part II

  • Classification is the task of predicting the label or target group of an object, using its attributes or features. Classification task can be accomplished through various supervised machine learning techniques. We also discuss Ensemble techniques. Ensemble technique combines the decisions of multiple techniques and tries to improve the performance of the task, for example, regression or classification. This module covers various classification and ensemble techniques, along with relevant concepts.

Classification Techniques

  • Logistic Regression
  • Decision Trees
  • Support Vector Machines

Evaluating Classification Models

  • K-fold validation
  • ROC and AUC Metric
  • Confusion Matrix

Ensemble Techniques

  • Random Forest
  • Bagging
  • Boosting
  • Stochastic Gradient Boosting

Module: 4- AI - Supervised Machine Learning & Applications - Part II

  • Classification is the task of predicting the label or target group of an object, using its attributes or features. Classification task can be accomplished through various supervised machine learning techniques. We also discuss Ensemble techniques. Ensemble technique combines the decisions of multiple techniques and tries to improve the performance of the task, for example, regression or classification. This module covers various classification and ensemble techniques, along with relevant concepts.

Classification Techniques

  • Logistic Regression
  • Decision Trees
  • Support Vector Machines

Evaluating Classification Models

  • K-fold validation
  • ROC and AUC Metric
  • Confusion Matrix

Ensemble Techniques

  • Random Forest
  • Bagging
  • Boosting
  • Stochastic Gradient Boosting

Module: 5- AI - Unsupervised Machine Learning & Applications

  • Clustering is an unsupervised machine learning technique used to identify groups of objects such that the objects are of a group are more similar to each other than to objects of other groups. This module discusses various clustering concepts and techniques in detail.

Clustering Techniques

  • K-means
  • Hierarchical
  • DBSCAN

Clustering Concepts

  • Distance Types in Clustering
  • Clustering Performance Measures

Module: 6- AI - Machine Learning Techniques for Text Data

  • In the current business environment, abundant unstructured data is available to organizations. In this unstructured data a large amount of data is in the form of text. The text data from within the organization, social media and external environment can be utilized to generate insights. Text analytics process can be used to build corpus and build representation of textual data for various text analytics based applications such as sentiment analysis, topic modelling, text clustering among others.

Text Analytics Process & Applications

Building Corpus

Corpus Pre-Processing

Sentiment Analysis

Topic Modelling

Text Clustering

Module: 7- AI – Deep Learning & Applications - Part I

  • Deep learning based models are used to generate insights, answer business problems and build new products/services. Deep learning models utilizes neural network architecture and are used to provide solutions to tasks such as, speech recognition, image classification and handwriting transcription among others. Deep learning utilizing keras and tensorflow can lead to required data representations based on neural networks.

Introduction to Neural Networks

Tensors & Tensor Operations

TensorFlow & Keras

Deep learning & Anatomy of Neural Networks

Recurrent Neural Networks

Module: 8- AI – Deep Learning & Applications - Part II

  • Computer vision (CV) technique enables computers to understand and interpret the visual objects. Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision to create real-world applications like medical discoveries, visual recognition systems, self-driving cars, emerging e-commerce and many more. Natural Language Processing (NLP) enables machines to understand, analyse, and generate natural languages.

Convolutional Neural Networks

  • CNN overview
  • Convolutional Layer
  • Pooling Layer
  • CNN Model Architecture
  • Building CNN Model

Image Analytics

  • Understanding Image
  • Image Formation
  • Image Segmentation
  • Pattern Recognition in Image
  • Image Pre-processing and Features Extraction

Computer Vision

  • Basic Concepts
  • Business Applications
  • Learning and Inference in Vision
  • Models for Computer Vision
  • TensorFlow Recognition Application
  • Challenges and Risks Associated with Implementing Computer Vision

Natural Language Processing

  • Fundamentals of NLP
  • Business Applications
  • Building NLP System

Module: 9- Capstone Project

Capstone Project

Capstone Project Review

Capstone Project Presentations

Synthesis

30 Hrs. Tableau Masterclass by Nulearn

Introduction

  • Introduction to Tableau
  • Data Connection
  • Tableau Environment

Basic Charts

  • Text Tables
  • Highlight Tables
  • Pie Charts
  • Bar Chart
  • Stacked Bar Chart
  • Side by Side Bar Chart
  • Circle Chart
  • Side by Side Circle Chart
  • Bubble Chart

Advanced Charts - I

  • Heat Map
  • Tree Map
  • Area Chart Discrete
  • Area Chart Continuous
  • Bullet Graph
  • Symbol Map
  • Filled Map
  • Histogram & Bins

Advanced Charts - II

  • Box and Whisker
  • Line Chart Continuous
  • Line Chart Discrete
  • Dual Line Chart
  • Dual Combination
  • Scatter Plot

Customizing Charts

  • Percentage Of
  • Totals
  • Filters & Highlighters
  • Groups
  • Hierarchy
  • Sets
  • Parameters
  • Calculated Fields
  • Dashboard
  • Storyboard

Campus Component

5 days campus visit at IIM Kashipur

How it Work

The programme is a blended  comprising both online and on-campus modules. For the online modules, the primary method of instruction will be through LIVE lectures that will be delivered online via the internet to participants’ desktops/laptops or classrooms. The lectures will be delivered by eminent faculty from IIM Kashipur and expert(s) from the industry. The programme will be primarily taught through a combination of class exercises, presentations, take-home exercises, simulation and case studies. The program contents are organized in a way to provide the participants with an introduction on the application of content to various business aspects.

Program Faculty

Dr. Rajiv Kumar- PhD - IIM Ranchi

Dr. Abhradeep Maiti- PhD - Middle Tennessee State University USA

Dr. Harish Kumar- PhD - IIT Delhi

Dr. Mayank Sharma- PhD - IIM Lucknow

Dr. Sabyasachi Patra- PhD - IIT Kanpur

Dr. Venkataraghavan K- PhD - IIT Madras

Total Fees

For Indian Residents - INR 1,60,000/- plus GST as applicable 

Campus Fee (If Campus Visit Happens) INR 40,000 + GST*
Alumni Fee (Mandate) INR 10,000 + GST*

For International Students - USD$ 5000/-

No. of INSTALLMENTS

Total amount to be paid in 5 installments

Instalment Structure

First Installment Second Installment Third Installment Fourth Installment Fifth Installment
INR 30,000 INR 40,000 INR 30,000 INR 30,000 INR 30,000
USD 1000 USD 1000 USD 1000 USD 1000 USD 1000

Technological Partner

Nulearn

Review

The programme walks you through theoretical knowledg of Artificial Intelligence and Machine Learning with compusory application and practical work. The faculty designed this programme in a way that candidates can equip themselve with AI amd ML skillset while working with the help of LIVE sessions, assignments, projects, case studies and capstone project.

Applications can be submitted Online. Application Link can be shared with candidates post profile confirmation by the concerned team.

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Course Duration

12 Months

Start - 11th April 2021

Sunday: 11:00 AM - 2:00 PM and 1st & 3rd Saturday: 9:00 AM - 12:00 PM

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