machine-learning algorithm

1. Source Materials
1- “ UNDERSTANDING MACHINE LEARNING From Theory to
Algorithms” , Shai Shalev-Shwartz, Shai Ben-David, 2014.
2- “Introduction to Machine Learning” , Ethem Alpaydın, Third
Edition, 2014.

2. Introduction
Welcome
Machine Learning

3. What do you need to know now?
•You should know how to do math and how to program:
–Calculus
–Probability/statistics
–Linear algebra (matrices and vectors)
Programming:
1- Matlab.
2- TensorFlow is a free and open-source software library for machine learning.

4. Learning:
Learning is the process of converting experience into knowledge. The input to a learning algorithm is training data, representing
experience, and the output is some expertise, which usually takes the form of another computer program that can perform some
task.
TYPES OF LEARNING :
1- Supervised versus Unsupervised.
2- Active versus Passive Learners:
An active learner interacts with the environment at training time, say, by posing queries or performing experiments, while a
passive learner only observes the information provided by the environment (or the teacher) without influencing or directing
it.

5. Machine learning can be viewed as a branch of AI (Artificial Intelligence)

6. What is Machine Learning?
Machine learning is about the execution of learning by computers;
Definition of Machine Learning
Arthur Samuel (1959): Machine Learning is the field of study that gives the
computer the ability to learn without being explicitly programmed.
Machine learning is making decisions or predictions based on data.
Application of machine learning methods to large databases is called data mining.

7. Why is Machine Learning Important?
Some tasks cannot be defined well, except by examples (e.g., recognizing people).
Relationships and correlations can be hidden within large amounts of data. Machine
Learning/Data Mining may be able to find these relationships.
Human designers often produce machines that do not work as well as desired in the
environments in which they are used.
The amount of knowledge available about certain tasks might be too large for explicit
encoding by humans (e.g., medical diagnostic).
Environments change over time.
New knowledge about tasks is constantly being discovered by humans. It may be difficult to
continuously re-design systems “by hand”.

8. What is Machine Learning (speech recognition)?
“Hi”
“How are you”
“Good bye”
You said “Hello”
A large amount of
audio data
You write the
program for learning.
Learning ......

9. “monkey”
“cat
”
“dog”
What is Machine Learning?
This is “cat”
A large amount of
images
You write the
program for learning.
Learning ......

10. Machine Learning
≈ Looking for a Function
• Speech Recognition
• Image Recognition
• Playing Go
• Dialogue System
( )=
f
( )=
f
( )=
f
( )=
f
“Cat”
“How are
you”
“5-5”
“Hello”
“Hi”
(what the user said) (system response)
(next move)

11. Framework
A set of
function 
2
1, f
f
( )=
1
f “cat
”
( )=
1
f “dog
”
( )=
2
f “monkey”
( )=
2
f “snake
”
Model
( )=
f “cat”
Image Recognition:

12. Framework
A set of
function 
2
1, f
f
( )=
f “cat”
Image Recognition:
Model
Training
Data
Goodness of
function f
Better!
“monkey” “cat “dog
function input:
function

13. Framework
A set of
function 
2
1, f
f
( )=
f “cat”
Image Recognition:
Model
Training
Data
Goodness of
function f
“monkey” “cat “dog
*
f
Pick the “Best” Function
Using 
f
“cat”
Training Testing
Step 1
Step 2 Step 3

14. Step 1:
define a set
of function
Step 2:
goodness of
function
Step 3: pick
the best
function
Machine Learning is so simple ……