Machine Learning with Business Applications

Venue/Deadlines Program Dates Program Fees
Venue : IIMB Campus
Early Bird Discount Date : 13 Jan, 2025
Last date for registration: 24 Jan, 2025


Start Date : 03 Feb, 2025
End Date : 07 Feb, 2025

Residential Fee(excluding GST) :  Rs. 1,45,000
Residential Early Bird Fee(excluding GST) :  Rs. 1,30,500
Non-Residential Fee(excluding GST) :  Rs. 1,20,000
Non-residential Early Bird Fee(excluding GST) :   Rs. 1,08,000

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About IIM Bangalore

Who Should Participate

Managers and decision makers with roles in analytics and AI-based consulting in marketing, operations, supply chain management, finance, insurance, and general management in various industries should attend the course.   The course is suitable for those who are already working on ML to enhance their knowledge and for those with analytical aptitude and would like to start a new career in Analytics.

Important Deadlines

Venue : IIMB Campus
Early Bird Discount Date : 13 Jan, 2025
Last date for registration: 24 Jan, 2025

Contact Us

Ms. Preethi
Landline No.:+91-80-26993375
Mobile No. +91-8951974073
Email: preethi.s@iimb.ac.in

Starting In
Short Duration
International Travel
Alumni Status

Programme Overview
Machine Learning algorithms are part of Artificial Intelligence (AI) that imitates the human learning process, which can be used for decision making and problem solving. ML algorithms are systems of problem-solving techniques that exhibit human-like learning capability. While humans learn through practice and experience, machines learn through data. ML algorithms have applications across various industries and different functional areas. The primary objective of ML is to assist in decision making. Today, ML is used for driving innovation and as competitive strategy by several organizations.

The theory of bounded rationality proposed by Nobel Laureate Herbert Simon is evermore significant today with increasing complexity of business problems; limited ability of the human mind to analyze alternative solutions, and the limited time available for decision making. Introduction to Enterprise Resource Planning (ERP) systems has ensured availability of data in many organizations; however, traditional ERP systems lacked data analysis capabilities that can assist the management in decision making. ML assists companies with Robotic Process Automation (RPA) and derives cognitive insights.

Several reports have claimed that AI and Machine Learning specialists in Silicon Valley with few years of experience are paid $300,000 to $500,000 a year1. Bernard Marr, in his article published in the Forbes magazine, claimed that 74% of the customers will be happy to receive computer-generated insurance advice2. While using ML algorithms, we develop several models that can run into several hundreds and each model is treated as a learning opportunity. ML algorithms are classified as follows:
1. Supervised Learning Algorithms,
2. Unsupervised Learning Algorithms,
3. Reinforcement Learning Algorithms, and
4. Evolutionary Learning Algorithms.

In this executive education programme, we discuss various Machine Learning algorithms with their applications using case studies from various industries. The learning pedagogy includes hands-on sessions for better understanding of how ML is used for solving real-life problems.

Programme Objectives:
The course is designed to provide in-depth knowledge of ML algorithms that can be used for fact-based decision-making using case studies from Indian and multinational companies and understand how ML algorithms are used for automation and innovation. Primary objectives of the course are as follows:

  • Understand various ML algorithms such as supervised, unsupervised, and reinforcement algorithms.
  • Learn to analyse data to gain insights using an appropriate ML algorithm under a given business context.
  • Learn various supervised learning algorithms such as regression, logistic regression, decision tree learning, random forest, boosting, neural networks, and deep learning algorithms with applications in solving managerial problems.
  • Learn unsupervised learning algorithms such ask-means clustering and factor analysis and its applications.
  • Understand how reinforcement and evolutionary algorithms are used by organisations, especially in automation.
  • Understand applications of ML in functional areas such as marketing, finance, operations, and supply chain and HR.
  • Analyse and solve problems from different industries such as e-commerce, insurance, manufacturing, service, retail, software, banking and finance, sports, pharmaceutical and aerospace using ML algorithms.
  • Hands-on experience with software such as Microsoft Excel, Evolver, R, Python, and other proprietary software.

Programme Content:
Supervised Learning Algorithms with Applications in Predictive Analytics:
Simple linear regression: coefficient of determination, significance tests, residual analysis, confidence and prediction intervals. Multiple Linear Regression (MLR): coefficient of multiple coefficient of determination, interpretation of regression coefficients, categorical variables, heteroscedasticity, multicollinearity, outliers, auto-regression and transformation of variables. MLR model development and feature selection. Application of supervised learning in solving business problems such as pricing, customer relationship management, sales and marketing.

Supervised Learning Algorithms with Applications in Classification Problems:
Logistic and Multinomial Regression: Logistic function, estimation of probability using logistic regression, Deviance, Wald test, Hosmer Lemeshow test. Feature selection in logistic regression. Ensemble Methods – Random Forest and Boosting. Business applications of classification problems such as sales conversion, employee attrition, and B2B sales management.

Supervised Learning Algorithms for Forecasting:
Moving average, exponential smoothing, Trend, cyclical and seasonality components, ARIMA (autoregressive integrated moving average), and ARIMAX models. Application of Supervised Learning Algorithms in retail, direct marketing, health care, financial services, insurance, supply chain etc

Unsupervised Learning Algorithms:
Clustering: K-means and Hierarchical

Neural Networks and Deep Learning:
Introduction to Neural Networks: Multilayer perceptron; Backpropagation Algorithms. Deep Learning Algorithms: Convolutional Neural Networks (CNN) and Recrurrent Neural Networks (RNN)

Reinforcement Learning Algorithms:
Markov Chains, Markov Decision Process, Policy Iteration and Value Iteration Algorithms with applications in marketing and finance.

Natural language processing, Text mining and sentiment analysis; Naive Bayes Algorithm.

The following case studies published by the program director at the Harvard Business Publishing will be discussed during the course:

  • Package Pricing at Mission Hospitals
  • Marketing Head’s Conundrum
  • Breaking Barriers – Micro Mortgage Analytics
  • Consumer Analytics at Big Basket – Product Recommendations
  • Customer Analytics at Flipkart.Com
  • Forecasting Demand for Food at Apollo Hospitals
  • HR Analytics at Scaleneworks – Behavioural Modelling to Predict Renege
  • Predicting Earnings Manipulations by Indian Firms Using Machine Learning Algorithms
  • Machine Learning Algorithms to Drive CRM in the online E-commerce site at VMWare
  • Consumer choice between house brands and national brands in detergent purchases at Reliance Retail
  • Improving lead generation at Eureka Forbes using Machine Learning Algorithms
  • Enhancing visitor experience at ISKCON using Text Analytics


Programme Director

Professor U Dinesh Kumar is a Professor in Decision Sciences and Information Systems at Indian Institute of Management Bangalore. He is currently the president of Analytics Society of India. U Dinesh Kumar holds a Ph.D. in Mathematics from IIT Bombay and M.Sc. in Applied Sciences (Operations Research) from P.S.G. College of Technology, Coimbatore. Dr Dinesh Kumar has over 20 years of teaching and research experience. Prior to joining IIM Bangalore, Dr Dinesh Kumar has worked at several reputed Institutes across the world including Stevens Institute of Technology, USA; University of Exeter, UK; University of Toronto, Canada; Federal Institute of Technology, Zurich, Switzerland; Queensland University of Technology, Australia; Australian National University, Australia and the Indian Institute of Management Calcutta.

Dr Dinesh Kumar has published more than 60 research articles in leading academic journals.  Twenty eight of his case studies on Business Analytics based on Indian and multinational organizations such as Aavin Milk Dairy, Apollo Hospitals, Bigbasket, Bollywood, Flipkart.com, Hewlett and Packard, Larsen & Toubro, Manipal Hospitals, Mission Hospital, Hindustan Aeronautics Limited, Indian Premier League, Reliance Retail, Shubham Housing Finance Limited and VMWare have been published at the Harvard Business Publishing’s case portal.  He has authored 3 books, his recent book is titled, “Business Analytics – The Science of Data Driven Decision Making”, published by Wiley in 2017.

Dr Dinesh Kumar has carried out predictive and prescriptive analytics consulting projects for organizations such as The Boston Consulting Group (India) Private Limited, Hindustan Aeronautics Limited, Qatar Airways, Mission Hospital, Manipal Hospitals, Scalene Works, Wipro Limited, UNIBIC and the World Health Organization etc.

Dr Dinesh Kumar has conducted training program on Analytics for several companies such as Accenture, Aditya Birla Group, Ashok Leyland, Bank of America, Blue Ocean Market Intelligence, Cisco, Fidelity, Hindustan Aeronautics Limited, Honey Well, Infosys, ITC Info Tech, Ocwen financial Services and etc

Dr Dinesh Kumar conducts corporate training programme in Analytics and trained more than 1000 professionals in the field of analytics.  He is the founding president of the Analytics Society of India (ASI).  Dr Dinesh Kumar was awarded the Best Young Teacher Award by the Association of Indian Management Institutions in 2003.  He is listed as one of the top 10 analytics academics in India by the analytics India magazine.

Participant Benefits

Participant Benefits
As a participant of this Short Duration Programme, you will be able to enjoy some exclusive benefits other than the outcomes such as skills and knowledge enhancement and building specific competencies that can help shape your career growth.

Some of the exclusive benefits of attending this programme are listed below –

  • Receive Executive Education eNewsletters
  • Invitation to share articles to the EEP blog (subject to a shortlisting process
  • Participate in EEP webinars on various topics
  • Invitation to curated events and programs by the EEP office

Programme Charges

Programme Fee
INR 1,45,000/- Residential and INR 1,20,000/- Non -Residential (+ Applicable GST) per person for participants from India and its equivalent in US Dollars for participants from other countries.

Early Bird Discount
Nominations received with payments on or before 13-Jan-25 will be entitled to an early bird Discount of 10%.
Early Bird Fee (Residential) INR 1,30,500/-(+ Applicable GST)
Early Bird Fee (Non-Residential) INR 1,08,000/- (+ Applicable GST)

Group Discount
Group Discount of 5% percentage can be availed for a group of 3 or more participants when nominations received from the same organization.

Please Note

  • The programme fee should be received by the Executive Education Office before the programme commencement date.
  • In case of cancellations, the fee will be refunded only if a request is received at least 15 days prior to the start of the programme.
  • If a nomination is not accepted, the fee will be refunded to the person/ organisation concerned.
  • All enrolments are subject to review and approval by the programme All enrolments are subject to review and approval by the programme director. Joining Instructions will be shared with the organisation if sponsored or to the participants on selection.
  • Kindly do not make your travel plans unless you receive the offer letter from IIMB.
  • A certificate of participation will be awarded to the participants by IIMB.

Certificate Sample

Note: Certificate image is for reference to potential participants only and may change at the discretion of Executive Education Programmes Office

How To Apply for the Programme

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