Short Description about machine learning.What is machine learning? specifications , categories, terminologies and applications every thing is explained in short way.
3. About
Subfield of Artificial Intelligence (AI)
Name is derived from the concept that it deals with
“construction and study of systems that can learn from data”
Can be seen as building blocks to make computers learn to behave more
intelligently
It is a theoretical concept. There are various techniques with various
implementations.
http://en.wikipedia.org/wiki/Machine_learning
In other words:
“A computer program is said to learn from experience (E) with some
class of tasks (T) and a performance measure (P) if its performance
at tasks in T as measured by P improves with E”
4. Definitions
“Learning is any process by which a system improves performance from experience.”
(By Herbert Simon)
Definition by Tom Mitchell (1998):
Machine Learning is the study of algorithms that
improve their performance P
at some task T
with experience E.
A well-defined learning task is given by <P, T, E>.
Machine learning investigates the mechanisms by which knowledge is acquired through
experience
Machine Learning is the field that concentrates on induction algorithms and on other
algorithms that can be said to ``learn.''
5. When Do We Use Machine Learning
ML is used when:
Human expertise does not exist (navigating on Mars)
Humans can’t explain their expertise (speech recognition)
Models must be customized (personalized medicine)
Models are based on huge amounts of data (genomics)
6. Terminologies
Features
The number of features or distinct traits that can be used to describe
each item in a quantitative manner.
Samples
A sample is an item to process (e.g. classify). It can be a document, a
picture, a sound, a video, a row in database or CSV file, or whatever
you can describe with a fixed set of quantitative traits.
Feature vector
is an n-dimensional vector of numerical features that represent some
object.
7. Terminologies Conti….
Feature extraction
Preparation of feature vector
transforms the data in the high-dimensional space to a space of fewer
dimensions.
Training/Evolution set
Set of data to discover potentially predictive relationships.
8. Lets go some what deeply…
What do you mean by
Apple
9. Learning (Training)
Example of Apple:…
Features:
1. Color:
Radish/Red
2. Type : Fruit
3. Shape
etc…
Features:
1. Sky Blue
2. Logo
3. Shape
etc…
Features:
1. Yellow
2. Fruit
3. Shape
etc…
11. Growth of Machine Learning
Machine learning is preferred approach to
Speech recognition, Natural language processing
Computer vision
Medical outcomes analysis
Robot control
Computational biology
12. Growth of Machine Learning Conti…
This trend is accelerating
Improved machine learning algorithms
Improved data capture, networking, faster computers
Software too complex to write by hand
New sensors / IO devices
Demand for self-customization to user, environment
It turns out to be difficult to extract knowledge from
human expertsfailure of expert systems in the 1980’s.
14. Learning Association
Basket analysis:
P (Y | X ) probability that somebody who buys X also buys Y where X and Y are
products/services.
Example: P ( chips | beer ) = 0.7
Market-Basket transactions
TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper, Coke
15. Supervised Learning
the correct classes of the training data are known
Credit: http://us.hudson.com/legal/blog/postid/513/predictive-
analytics-artificial-intelligence-science-fiction-e-discovery-truth
16. Supervised Learning: Uses
Prediction of future cases: Use the rule to predict the output for future
inputs
Knowledge extraction: The rule is easy to understand
Compression: The rule is simpler than the data it explains
Outlier detection: Exceptions that are not covered by the rule, e.g., fraud
Example: decision trees tools that create rules
17. Unsupervised Learning Conti…
the correct classes of the training data are not known
Credit: http://us.hudson.com/legal/blog/postid/513/predictive-
analytics-artificial-intelligence-science-fiction-e-discovery-truth
18. Unsupervised Learning
Learning “what normally happens”
No output
Clustering: Grouping similar instances
Other applications: Summarization, Association Analysis
Example applications
Customer segmentation in CRM
Image compression: Color quantization
Bioinformatics: Learning motifs
19. Semi-Supervised Learning
A Mix of Supervised and Unsupervised learning
Credit: http://us.hudson.com/legal/blog/postid/513/predictive-
analytics-artificial-intelligence-science-fiction-e-discovery-truth
20. Reinforcement Learning
allows the machine or software agent to learn its behavior based on
feedback from the environment.
This behavior can be learnt once and for all, or keep on adapting as time
goes by.
Credit: http://us.hudson.com/legal/blog/postid/513/predictive-
analytics-artificial-intelligence-science-fiction-e-discovery-truth
21. Reinforcement Learning
Topics:
Policies: what actions should an agent take in a particular situation
Utility estimation: how good is a state (used by policy)
No supervised output but delayed reward
Credit assignment problem (what was responsible for the outcome)
Applications:
Game playing
Robot in a maze
Multiple agents, partial observability, ...
23. Techniques
classification:
predict class from observations
clustering:
group observations into “meaningful” groups
regression (prediction):
predict value from observations
24. Classification
Example: Credit scoring
Differentiating between
low-risk and high-risk
customers from their
income and savings
Discriminant: IF income > θ1 AND savings > θ2
THEN low-risk ELSE high-risk
Model
25. Classification : Applications
Pattern recognition
Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair
style
Character recognition: Different handwriting styles.
Speech recognition: Temporal dependency.
Use of a dictionary or the syntax of the language.
Sensor fusion: Combine multiple modalities; eg, visual (lip image) and
acoustic for speech
Medical diagnosis: From symptoms to illnesses
Web Advertising: Predict if a user clicks on an ad on the Internet.
26. Face Recognition
In Face Recognition there is some raw data for the detection of any person’s
mood.
System will be trained through this data and it will give out put in percentages.
Training examples of a person
Test images
27. Clustering
Clustering is the task of grouping a set of objects in such a way
that objects in the same group (called a cluster) are more similar
to each other
objects are not predefined
For e.g. these keywords
“man’s shoe”
“women’s shoe”
“women’s t-shirt”
“man’s t-shirt”
can be cluster into 2 categories “shoe” and “t-shirt” or “man”
and “women”
Popular ones are K-means clustering and Hierarchical clustering
28. K-means Clustering
partition n observations into k clusters in which each observation belongs to
the cluster with the nearest mean, serving as a prototype of the cluster.
http://en.wikipedia.org/wiki/K-means_clustering
http://pypr.sourceforge.net/kmeans.html
29. Hierarchical clustering
method of cluster analysis which seeks to build a hierarchy of clusters.
There can be two strategies
Agglomerative:
This is a "bottom up" approach: each observation starts in its own cluster,
and pairs of clusters are merged as one moves up the hierarchy.
Time complexity is O(n^3)
Divisive:
This is a "top down" approach: all observations start in one cluster, and splits
are performed recursively as one moves down the hierarchy.
Time complexity is O(2^n)
http://en.wikipedia.org/wiki/Hierarchical_clustering
30. Prediction : Regression
is a measure of the relation between the mean
value of one variable (e.g. output) and
corresponding values of other variables (e.g.
time and cost).
regression analysis is a statistical process
for estimating the relationships among
variables.
Regression means to predict the output
value using training data.
Popular one is Logistic regression (binary
regression)
http://en.wikipedia.org/wiki/Logistic_regression
31. Regression Application
Navigating a car: Angle of the steering wheel (CMU NavLab)
Kinematics of a robot arm
α1= g1(x,y)
α2= g2(x,y)
α1
α2
(x,y)
32. Classification vs Regression
Classification
Classification means to group the
output into a class.
classification to predict the type of
tumor i.e. harmful or not harmful
using training data
if it is discrete/categorical
variable, then it is classification
problem
Regression
Regression means to predict the
output value using training
data.
regression to predict the house
price from training data
if it is a real
number/continuous, then it is
regression problem.
