The document discusses machine learning versus statistical modeling, highlighting the advantages of machine learning in making predictions from large datasets without explicit programming. It emphasizes the evolution of machine learning from pattern recognition and its capabilities in predicting data on the fly, compared to the assumptions and mathematical foundations of statistical modeling. A business case is presented to illustrate the application of machine learning in predicting customer churn for a telecom company.

1. Machine Learning
Lunch & Learn - Session 1
Luis Borbon
27/06/2017

2. Table of contents
1. Why?
2. Applications
3. Data
4. Machine Learning
5. Machine Learning vs. Statistical Modelling

3. Why?

4. Opportunities

5. Threats

6. Applications

7. Image recognition

8. Virtual reality and speech recognition

9. Self learning robots and customer segmentation

10. Prediction and recommendation

11. Data

12. Second point

13. Machine Learning

14. an algorithm that can
learn from data
without relying on
rules-based
programming.

16. Machine Learning vs. Statistical
Modeling

17. Machine Learning
a subfield of computer science and artificial
intelligence which deals with building systems
that can learn from data, instead of explicitly
programmed instructions.
Statistical Modelling
a subfield of mathematics which deals with
finding relationship between variables to predict
an outcome.
Data types: Machine learning algorithms can predict data on the fly and are
capable of learning from trillions of observations, one by one. Machine learning is
best used with data that has a wide number of attributes and a high number of
observations.
Made Assumptions: Statistical models embody a set of assumptions concerning
the generation of the observed data and similar data from larger populations.
Machine learning, on the other hand, can be considered statistics minus checking
of models and assumptions and instead relying on the predictive accuracy of
models.

18. Predictive Power and Human Effort: Machine learning is the science of getting
computers to act without being explicitly programmed thus no human interaction. It
explores the study and construction of algorithms that can learn from and make
predictions on data. Statistical models are about mathematics and require the
modeler to understand the relation between variables before putting in the data.
Birthdays: Machine learning and statistical modeling were born in different eras.
Statistical modeling has been around for centuries. However, machine learning was
defined more recently by computer scientists like Arthur Samuel and Tom Mitchell
in the mid-to-late 1950’s and flourishing in the 1990’s, emerging from computer
science and evolving from the study of pattern recognition and computational
learning theory in artificial intelligence.

19. Business case
Case: Understand the risk level of customers churn over a period of time for a
Telecom company
Data Available: Two Drivers – A & B

20. Second point

21. ML vs. Stat
Machine Learning algorithms are wide range tools. Online Learning tools predict
data on the fly. These tools are capable of learning from trillions of observations
one by one. They make prediction and learn simultaneously.
Other algorithms like Random Forest and Gradient Boosting are also exceptionally
fast with big data. Machine learning does really well with wide (high number of
attributes) and deep (high number of observations). However statistical modeling
are generally applied for smaller data with less attributes or they end up over fitting.

22. Naming Convention