This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, classification, and regression. It defines supervised learning as using a predefined dataset to train an algorithm, such as identifying images as dogs, cats, etc. Unsupervised learning is defined as finding patterns in unlabeled data, such as grouping emails by topic. Classification is described as predicting a yes/no outcome like identifying an image as an apple or orange. Regression is defined as predicting a numeric value like estimating a house price. The document then discusses how these machine learning problems are solved using tools like a hypothesis, cost function, and gradient descent.

1. ML-Theory

2. A reminder of what is
machine learning and a
Question
Within the scope of this
training course machine
learning is either …

3. this

4. or this

5. the question
the first question to answer is
WHAT is “this” and “or this” ??
to answer this question
we have to define and
explain some terms…

6. Supervised learning : the first
this
it’s when some algorithm or a
program works on a predefined
dataset.
ex: telling whether an image is for a
dog, cat, house or a car?

7. the first this…

8. Unsupervised learning : the
second this
when a program learns all alone to
find patterns and relations between
different variables
ex: automatically grouping emails
based on words by topic it talks
about…personal, education,
shopping…etc

9. the second this…

10. end it also means this

11. Let’s forget the
unsupervised thing for a
long while 

12. And talk about Supervised:
classification
classification is like telling whether
an image is for apple or an orange
based on color and shape, which is
a “yes” or “no” question.

13. And talk about Supervised:
classification (cont…)

14. which also means this…

15. And talk about Supervised:
regression
Regression is more like answering
the “how much” or “how many” like
predicting the price of a house.

16. And talk about Supervised:
regression (cont…)

17. And talk about Supervised:
regression (cont…)

18. which also means this…

19. So far…the answer to the what
question??
-supervised learning
-unsupervised learning
-classification
-regression
-features …. by intuition @_@

20. after what?.....now the
how???

21. a plan

22. the tools

23. the objective…

24. cost function

25. all together

26. answering the how??
-hypothesis
-plan
-hypothesis ….how data looks like
-cost function
-gradient descent

27. This is called single variable
linear regression…next?
-many variables (aka features)
-putting it into action using MATLAB
-nonlinear regression
-problems always emerge 
-- overfitting
-- underfitting
- and a solution  regularization

28. thank you!