1. FOUNDATIONS OF BUSINESS ANALYTICS Introduction to Machine Learning
Dr. M. Ramasubramaniam, Loyola Institute of Business Administration, Chennai, India

2. Consumer: Imagine you are in Florida Hurricane, what will you buy?
Hurricane Analytics
Hurricane Analytics
Consumer: Imagine you are in Florida Hurricane, what will you buy?
Manager: If you are a Walmart Store manager what will you stock?
Property agent: If you are an insurance company employee how will value the damages to the property?
Policymaker: How will the structure of a location effect the damages caused by a hurricane?
Is there a better way to prepare for a hurricane onslaught?
and

3. Food Checklist before Hurricane
Food Checklist (Predictive!)
Food Checklist before Hurricane
Bottled Water (1 Gallon of water/person)
Cereal/Granola
Peanut Butter (Good source of healthy fat and protein)
Apples (Long shelf life)
Dry fruits and Nuts
Instant Coffee
Canned vegetables, Black Beans (Can be eaten on their own) , Tuna (Canned Tuna don’t need refrigeration/cooking and also good source of protein)
Beer!!

4. How does Autonomous Vehicles Work
Deep Learning
Deep reinforcement learning
Convolutional neural nets
Recurrent neural nets

5. Machine Learning
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
It is multi-disciplinary field. It draws on results from Artificial Intelligence, Probability and Statistics, Control theory, Information Theory, Psychology and Philosophy.

6. Machine Learning and Data Mining
Most intelligence tasks that require an ability to induce new knowledge from past experience.
Machine learning has this characteristic. It involves study of algorithms (i.e. computer programs) that can extract information automatically. An example of ML algorithm can be a Kalman filter.
Data Mining is an area which takes inspiration and techniques from ML but is put to different ends. It is normally carried by a person, in a specific situation, in a specific dataset, with a goal in mind.
This person want to use ML techniques for purpose of generating insights in an area where there was little knowledge beforehand.

7. Model fitted is based on the training sample
Assumption behind ML
Training sample and test or future samples will follow a similar distribution
Model fitted is based on the training sample
The function fitted is always different from the ideal target function

8. A Tour of Machine Learning Algorithms
Categorize based on:
Learning Style
Similarity (form or function)

9. Algorithms by Learning Style
Supervised Learning
Unsupervised Learning

10. Algorithms by Similarity
Regression Algorithms
Modeling relationship with variables with DV a numeric variable
Ordinary Least Squares Regression (OLSR)
Linear Regression
Logistic Regression
Stepwise Regression
Locally Estimated Scatterplot Smoothing (LOESS)

11. Instance-based Algorithms
Does prediction by closely matching input with database.
K-NN
Learning vector Quantization
Locally weighted learning (LWL)

12. Regularization Algorithms
Method based on Penalty function for Model complexity
Method favors simpler models that are generalizable
Ridge regression
Least Absolute Shrinkage and Selection Operator (LASSO)
Elastic Net
Least-Angle Regression (LARS)

13. Decision Tree Algorithms
Methods construct a model of decisions based on actual values
They create a tree structure until a prediction decision is made for a given record.
CART
C4.5 and C5.0
Chi-square Automatic Interaction Detection (CHAID)
Conditional Decision Trees

14. Bayesian Belief Network
Bayesian Algorithms
Explicitly apply Bayes’ theorem for problems such as classification and regression
Naïve Bayes
Gaussian Naïve Bayes
Bayesian Belief Network

15. Clustering Algorithms
Distance based grouping of similar objects
Hierarchical clustering
K-Means

16. Association Rule learning
Extract rules that best explain observed relationships between variables
Apriori algorithm
Eclat algorithm

17. Artifical Neural Networks
Inspired by function of biological neural networks
Basically pattern matching algorithms
Useful for both regression and classification
Perceptron
Back-propogation
Hopfield network
Radial basis function network

18. Deep Learning Algorithms
Modern version of Neural nets
Exploit abundant cheap computation
Deep Boltzmann Machine
Deep Belief Networks
Stacked Auto-encoders

19. Dimensionality Reduction Algorithms
Exploits inherent structure in the data
Simplifies data
Principal Component Analysis (PCA)
Multi-dimensional Scaling
Linear Discriminant Analysis

20. Gradient Boosting Machines Random Forest
Ensemble Algorithms
Methods comprise of multiple weaker models that are independently trained and predictions are combined in some way to make overall prediction
Bagging and Boosting
Gradient Boosting Machines
Random Forest
Extreme Gradient Boosting (XGBoost)

21. Supervised vs. Unsupervised Methods
Data available on target
Goal is to fit a target function with the given data
Unsupervised
Data not available on target
Interest is to look for between groups heterogeneity and within group similarity

22. Data Mining Results