This presentation is about ML

1. An Overview of
Machine Learning

2.  What is machine learning?
 Learning and Inference model
 Applications
 Algorithms
 Conclusion
Outline & Content

3.  What is meant by machine learning
algorithms?
Machine learning is an application of artificial
intelligence (AI) that provides systems the ability
to automatically learn and improve from
experience without being explicitly
programmed.
Machine learning focuses on the development
of computer programs that can access data and
use it learn for themselves.
What is machine learning?

4. Machine learning is programming computers to optimize a
performance criterion using example data or past experience.
-- Ethem Alpaydin
The goal of machine learning is to develop methods that can
automatically detect patterns in data, and then to use the
uncovered patterns to predict future data or other outcomes of
interest.
-- Kevin P. Murphy
The field of pattern recognition is concerned with the automatic
discovery of regularities in data through the use of computer
algorithms and with the use of these regularities to take actions.
-- Christopher M. Bishop
What is machine learning?

5.  Machine learning is about predicting the
future based on the past.
-- Hal Daume III
What is machine learning?

6. Traditional Programming
Machine Learning
Computer
Data
Program
Output
Computer
Data
Output
Program

7. Prediction
Learning and Inference model
Training
Labels
Training
Images
Training
Training
Image
Features
Image
Features
Testing
Test Image
Learned
model
Learned
model

8.  Machine learning is preferred approach to
 Speech recognition
 Natural language processing
 syntactic pattern recognition
 Computer vision
 Medical outcomes analysis
 Robot control
 Computational biology
 Search engines
Sample Applications

9.  The success of machine learning system also depends
on the algorithms.
 The algorithms control the search to find and build the
knowledge structures.
 The learning algorithms should extract useful information
from training examples.
Algorithms

10.  The basics of machine learning, here is a brief discussion on the top machine
learning algorithms used by data scientists.
 Machine Learning algorithms are classified as :
1) Supervised Machine Learning Algorithms: Machine learning algorithms that make
predictions on given set of samples. Supervised machine learning algorithm searches for
patterns within the value labels assigned to data points.
Note:(Training data
includes desired outputs)
Algorithms

11.  Supervised learning problems can be further grouped
into regression and classification problems.
 Classification: A classification problem is when
the output variable is a category, such as “red” or
“blue” or “disease” and “no disease”.
 Regression: A regression problem is when the
output variable is a real value, such as “dollars” or
“weight”.
Supervised Learning

12.  In order to solve a given problem of supervised learning, one has to perform the
following steps:
 Determine the type of training examples.
 ) Before doing anything else, the user should decide what kind of data is to be used as a training set.
In case of handwriting analysis, for example, this might be a single handwritten character, an entire
handwritten word, or an entire line of handwriting.(
 Gather a training set.
 The training set needs to be representative of the real-world use of the function. Thus, a set of input
objects is gathered and corresponding outputs are also gathered, either from human experts or from
measurements.
 Determine the input feature representation of the learned function.
 The accuracy of the learned function depends strongly on how the input object is represented.
Typically, the input object is transformed into a feature vector, which contains a number of features that
are descriptive of the object. The number of features should not be too large, because of the curse of
dimensionality; but should contain enough information to accurately predict the output.
 Determine the structure of the learned function and corresponding learning algorithm.
 For example, the engineer may choose to use support vector machines or decision trees.
 Complete the design. Run the learning algorithm on the gathered training set. Some supervised
learning algorithms require the user to determine certain control parameters.
 Evaluate the accuracy of the learned function.
 After parameter adjustment and learning, the performance of the resulting function should be
measured on a test set that is separate from the training set.
Supervised Learning

13.  For Example:
 Based on some prior knowledge (when its sunny,
temperature is higher; when its cloudy, humidity is
higher, etc.) weather apps predict the parameters
for a given time. (Regression)
 Based on past information about spams, filtering
out a new incoming email into Inbox (normal)
or Junk folder (Spam) . (Classification)
Supervised Learning

14. 2) Unsupervised Machine Learning Algorithms
There are no labels associated with data points. These
machine learning algorithms organize the data into a group of
clusters to describe its structure and make complex data look
simple and organized for analysis.
 Note:(Training data does
not include desired outputs)
Algorithms

15.  Unsupervised learning problems can be further grouped into
clustering and association problems.
 Clustering: A clustering problem is where you want to discover
the inherent groupings in the data, such as grouping customers
by purchasing behavior.
(Clustering: grouping similar instances)
 Example applications
 Clustering items based on similarity
 Clustering users based on interests
 For Example:
 A friend invites you to his party where you meet totally strangers.
Now you will classify them using unsupervised learning (no prior
knowledge) and this classification can be on the basis of gender,
age group, dressing, educational qualification or whatever way
you would like.
Unsupervised Learning

16. 3) Reinforcement Machine Learning
Algorithms
These algorithms choose an action, based on each data
point and later learn how good the decision was.
 Over time, the algorithm changes its strategy to learn
better and achieve the best reward (Rewards from
sequence of actions)
 Reinforcement algorithms are not given explicit
goals; instead, they are forced to learn these
optimal goals by trial and error.
Algorithms

17. Reinforcement Machine Learning
Algorithms
Example: Game playing
Think of the classic Mario Bros. video game;
reinforcement learning algorithms would, by trial
and error, determine that certain movements
and button pushes would advance the player's
standing in
the game, and trial
and error would
aim to result in an
optimal state of
game play.

18. Reinforcement Machine Learning
Algorithms
 The reinforcement
learning model
prophesies
interaction between
two elements –
environment and
the learning agent.
The environment
rewards the agent
for correct actions,
which is the
reinforcement
signal. Leveraging
the rewards
obtained, the agent
improves its
environment
knowledge to select
the next action.

19.  List of Common Machine Learning Algorithms
 Naïve Bayes Classifier Algorithm. (Supervised learning algorithms)
 K Means Clustering Algorithm. (Unsupervised learning algorithms)
 Support Vector Machine Algorithm. (Supervised learning algorithms)
 Apriori Algorithm. (Unsupervised learning algorithms)
 Linear Regression. (Supervised learning algorithms)
 Logistic Regression. (Supervised learning algorithms)
 Artificial Neural Networks.
 Random Forests. (Supervised learning algorithms)
 Decision Trees (Supervised learning algorithms)
 The k-nearest neighbours algorithm (Supervised learning
algorithms)
What is the best machine learning
algorithms?

20.  We have a simple overview of some
techniques and algorithms in machine
learning. Furthermore, there are more and
more techniques apply machine learning
as a solution. In the future, machine
learning will play an important role in our
daily life.
Conclusion