This document provides an overview of statistical learning through a presentation. It defines statistical learning as using tools to model and understand complex datasets by blending statistics and machine learning. Statistical learning uses smaller datasets than machine learning. It focuses on inferences from samples and hypotheses, while machine learning emphasizes predictions from supervised and unsupervised learning. Examples are given of supervised learning involving predicting an outcome like income from attributes, and unsupervised learning like market segmentation to group customers. The Boston housing dataset is described as an example involving house prices predicted from housing attributes.

1. Presentation on
Statistical learning
Niladree Chowdhury

2. Outline
• Concept of statistical learning
• Statistical learning & machine learning
• Modelling
• Supervised vs Unsupervised learning
• Specific real life examples

3. Big Visual Data

4. Concept of Statistical Learning
• Statistical learning refers to the set of tools for
modelling and understanding complex datasets.
• It blends with parallel developments in computer
science in particular machine learning
• Both methods are data dependent. However,
Statistical Learning is based on a smaller dataset
with a few attributes, compared to Machine
Learning where it can learn from billions of
observations and attributes.

5. • Statistical Learning is mostly about inferences,
most of the idea is generated from the sample,
population, and hypothesis, while Machine
Learning emphasizes on predictions, supervised
learning, unsupervised learning.

6. Statistical Learning
• Suppose we observe and for
• We believe that there is a relationship between
Y and at least one of the X’s.
• We can model the relationship as
• Where f is an unknown function and ε is a
random error. Here f represents systematic
information x provides about y.
• Statistical learning refers to a set of
approaches for estimating f.
Xi = (Xi1,..., Xip )Yi
i =1,...,n
iii fY  )(X

7. Reasons of estimating
function
There are 2 reasons for estimating f,
• Prediction: If we can produce a good estimate
for f (and the variance of ε is not too large) we
can make accurate predictions for the
response, Y, based on a new value of X.
• Inference: We may also be interested in the
type of relationship between Y and the X's.

8. Supervised vs. Unsupervised
Learning
Statistical learning can be divided into two parts.
Supervised Learning:
• Supervised Learning is where both the predictors,
Xi, and the response, Yi, are observed.
• This is the situation you deal with in Linear
Regression classes
Unsupervised Learning:
In this situation only the Xi’s are observed.
• We need to use the Xi’s to guess what Y would
have been and build a model from there.
• A common example is market segmentation where
we try to divide potential customers into groups
based on their characteristics.
• A common approach is clustering

9. What’s Next?

10. Real life examples
• Income vs Years
of education
• Wage Data

11. • Boston Data
The dataset (Boston Housing Price) was taken from the StatLib library which
is maintained at Carnegie Mellon University and is freely available for
download from the UCI Machine Learning Repository. The dataset consists of
506 observations of 14 attributes. The median value of house price in
$1000s, denoted by MEDV, is the outcome or the dependent variable in our
model.
10 20 30
1020304050
lstat
medv

12. 4 5 6 7 8
1020304050
rm
medv
14 16 18 20 22
1020304050
ptratio
medv

13. Thank you