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Copy of Machine learning Lec

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Maram Gamal

on 8 October 2013

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Transcript of Copy of Machine learning Lec

Overview
Introduction.
Machine learning definition.
Why machine learning.
Applications of ML.
Learning paradigms.
Linear regression.
Cost function.
Gradient decent.
Machine learning definition
Machine Learning
Applications of ML
1- supervised learning
a- Regression example.
Arthur Samuel (1959) defined ML as the field of study that gives computers the ability to learn without being explicitly programmed.
Tom Mitchell (1998) defined well posed learning problem as : A computer program is said to learn from
experience E
with respect to some
task T
and some
performance measure P
, if its performance on
T
as measured by
P
improves with experience
E
.
Suppose your email program watches which email you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting?
T
E
P
Classifying emails as spam or not spam.
Watching you label emails as spam or not spam.
The number (or fraction) of emails correctly classified as spam/not spam.
None of the above this is not a machine learning problem.
Why machine learning ?
Need to make machines think and learn from mistakes like human.
To notice similarities between things and so generate new ideas.
Attempt to work out why things went wrong (Explanation).
Natural language processing
Search engines
Pattern recognition
Game playing
Robot locomotion
Medical diagnosis
Classifying DNA
Stock market analysis
Learning paradigms
1- Supervised learning
2- Unsupervised Learning
3- Reinforcement learning
- Learning to predict houses prices.
Suppose you collect statistics about how much houses cost according to the square footage of the house.
100
200
300
400
500
500
1000
1500
2000
2500
3000
0
Size in (feet)^2
Price in 1000's
a- Regression
b- Classification
1- Supervised learning

Given:
Data set (input & output)
Regression
---> the output you are trying to predict is a
continuous
value of the price.
b- Classification example.
- Let's say you collect a dataset on a cancer tumor and you want the program output to predict whether the certain tumor is malignant or benign.
+1
Malignant
Tumor size
More generally there's other examples that use more than 1 input variable (multi-i/p).
we can predict whether the tumor is malignant or benign through patient age, tumor size ..... so on.
Age
Tumor size
You’re running a company, and you want to develop learning algorithms to address each of two problems.

Problem 1:
You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months.
Problem2:
You’d like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised.
Should you treat these as classification or as regression problems?
Treat both as classification problems.
Treat problem 1 as a classification problem, problem 2 as a regression problem.
Treat problem 2 as a classification problem, problem 1 as a regression problem.
Treat both as regression problems.
2- Unsupervised learning
Given : Inputs only
Required: Cluster data into groups
Supervised learning
Unsupervised learning
Understanding genes data
Social network analysis
Astronomical data analysis
Market segmentation
Cocktail party problem
Speaker 2
Speaker 1
Microphone 1
Microphone 2
Microphone1:
Output 1:
Output 2:
Output 1:
Output 2:
Microphone2:
Microphone1:
Microphone2:
Of the following examples, which would you address using an unsupervised learning algorithm? (Check all that apply.)
Given email labeled as spam/not spam, learn a spam filter.
Given a set of news articles found on the web, group them into set of articles about the same story.
Given a database of customer data, automatically discover market segments and group customers into different market segments.
Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.
3- Reinforcement learning
No data set
Have only critic
Credit assignment problem
If you write a program to make an autonomous helicopter to fly, you notice that if you make a wrong decision on a helicopter, the consequence of crashing it may not happen until much later.
Good helicopter
Bad helicopter
Reward function
Example :-
Linear regression with one variable
Training set of housing prices
Area(x)
Price(y)
2104
1416
1534
852
400
232
315
178
m:-
Number of training examples.
X:-
Input variable/feature.
Y:-
Output variable/target.
In order to design a learning algorithm, the first thing we have to decide is how we want to represent the hypothesis.
X
Y
Using linear representation for the hypothesis as :
h(x)=
Ɵ + Ɵ
0
1
x
Adding another input(feature)
h(x)= Ɵ + Ɵ X+ Ɵ X
1
1
2
2
0
Any questions?
malignant
benign
Linear representation
h(x)=∑ Ɵ X
i=0
n
i
i
h(x) :- Actual output
Ɵ ,Ɵ ,Ɵ :- Learning algorithm parameters
0
1Ɵii
2
n:- Number of features (input)
for simplicity, let X =1 ,so
0
How do we choose the parameter theta so that our hypothesis will make accurate prediction?
we want to make that squared difference between the predicted price and the actual price (cost function) as small as possible
J(Ɵ)=1/2∑(h (x )-y)
Ɵ
i
i=0
m
i
2
Our goal is to minimize the cost function. The algorithm for minimizing is
gradient descent algorithm.
Next Section :)
Checker Game
The checkers example,
E
T

P
10000s games
is playing checkers
if you win or not
Learning types
Inductive
Deductive
Learning steps
1- Training
2- Validation
3- Application
T
T
V
Data set
Classification
---> output here is
discrete
values 0 or 1
Full transcript