A very simple, a high-level overview to Supervised learning using Decision Trees and Weka

1. Supervised
Learning
Senthil Kumaran

3. சுத்தம் ெசய்த ேடட்டா
மட்டும் ஊட்டி விடடா

4. உன் பஸ் இன் கண்டக்டர்
நான்

5. Machine Learning
● In Algorithmic Problem Solving, we use Algorithms to solve problems
● Example
○ Sorting
○ Graph Search.
■ Depth First Search
■ Breadth First Search
● In Machine Learning, we still use Algorithms
○ But traditional algorithms are not sufficient to solve the problem.
● Example - Real time Traffic Prediction.
● Example - Real time food order delivery
● Example - Real time Taxi Cab Calling
■ We need algorithms that are constantly changing without much
human interaction.
● In other words, Machine Learning

6. Supervised Learning
● Supervised learning is used whenever we want to predict a certain
outcome from a given input.
● We have examples of input/output pairs.
● Like a Supervisor / Teacher.
○ Input and Output Examples are given to Machine to Study
○ Machine Studies Input and Output Examples with the help of
algorithms taught by Supervisor.
● Supervisor gives Tests
○ Tests help the Machine to know if it can answer questions correctly
○ If machine gets a test wrong, it studies again.
○ If machine gets test right, it takes harder questions
Using Supervised Learning - Machine creates a
Model to answer unseen problems !

7. Unsupervised Learning
● No Supervision.
● Learning algorithm is just shown the input data and asked to extract
knowledge from this data.
● For certain kinds of problems, data helps automatically.
○ Like differentiate between Land and Water

8. Supervised Learning
on
Student
Performance
Dataset

9. Problem
If we give different data
about students, can a
machine predict their
academic
performance?

10. UCI Dataset
● https://archive.ics.uci.
edu/ml/datasets/Stu
dent+Performance

11. Student Performance

12. Attributes

13. Attributes

14. What is a Decision Tree?

15. Decision Tree
● Feed Machine
Learning Software
with clean data. (Ref
Slide 1)
● The Software will
build the Decision
Tree for you.

16. Decision Tree
● Use Software, give
the data.
● Train with more data.
● The Software will
build the Decision
Tree for you.

17. Building a Decision Tree with Data
●

18. Machine Learning Software Does that
for you. Like https://www.cs.waikato.ac.nz/~ml/weka/
●

19. Machine Learning Software Does that
for you. Like https://www.cs.waikato.ac.nz/~ml/weka/
●

20. Tutorial on using WEKA
● https://www.analyticsvidhya.com/
blog/2020/03/decision-tree-weka-
no-coding/

21. Decision Tree
●

22. Decision Tree
●

23. Student Performance
● With this data set,
student performance
is predicted for new
student by the
computer.

24. References
● Introduction to Machine Learning with Python by Sarah Guido;
Andreas C. Müller Published by O'Reilly Media, Inc., 2016
● https://towardsdatascience.com/a-brief-introduction-to-supervised-lear
ning-54a3e3932590
● Senthil Kumaran Georgia Tech Machine Learning Course Report.