Date: 24 – 29 February 2020
Machine learning algorithms are part of artificial intelligence (AI) that imitates the human learning process. Machines are more powerful than the human brain at analysing data and gain insights about the business. Machine Learning (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.
Program Objectives:
The course is designed to provide in-depth knowledge of Machine Learning Algorithms that can be used for fact-based decision-making using real case studies and understand how machine learning algorithms are used for automation and innovation. Primary objectives of the course are:
- Understand various machine learning algorithms such as supervised, unsupervised and reinforcement algorithms.
- Learn to analyse data to gain insights using an appropriate machine learning 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 problem.
- Learn unsupervised learning algorithms such as k-means clustering, factor analysis, multivariate Gaussian distribution and its applications in gaining insights from data.
- Understand how reinforcement and evolutionary algorithms are used by organizations.
- 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, aerospace etc using ML algorithms.
- Hands on experience with software such as Microsoft Excel, Evolver, R, Python and other proprietary software.
Coverage:
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.
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. Naïve Bayes Algorithm. Feature selection in logistic regression. Ensemble Methods – Random Forest and Boosting
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 Clustering and Hierarchical Clustering; Data Reduction Techniques: Factor Analysis; Anomaly detection: Multivariate Gaussian Distribution
Reinforcement Learning Algorithms
Markov Chains, Markov Decision Process, Policy Iteration and Value Iteration Algorithms with applications in marketing and finance
The following case studies will be discussed during the course.
- Predicting Net Promoter Score to Improve Patient Experience at Manipal Hospitals
- 1920 Evil Returns – Bollywood and Social Media Marketing
- 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
Venue : IIMB Campus
Programme Fee and Payment
INR 1,26,000/- Residential and INR 1,05,000/- Non -Residential (+ Applicable GST) per person for participants from India and its equivalent in US Dollars for participants from other countries.
Contact Information:
- Bannerghatta Road, Bengaluru-560076, India
- Phone: +91-80-26993264/3475
- Email: openpro@iimb.ac.in
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