This document discusses machine learning and artificial intelligence. It provides an overview of the machine learning process, including obtaining raw data, preprocessing the data, applying algorithms to extract features and train models, and generating outputs. It then describes different types of machine learning, including supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning. Specific algorithms like artificial neural networks, support vector machines, genetic algorithms are also briefly explained. Real-world applications of machine learning like character recognition and medical diagnosis are listed.

1. By
-Jigar Patel
M.Sc. Bioinformatics

2. What is Artificial intelligence?
Is AI different then machine learning techniques?
Basic Questions …….
Types of Machine Learning ?

3. Raw data
• The empirical data is obtained from sensors and
various other sources
Scaling
• The data is Scaled for the peak performance of the
Algorithm
Algorithms
• Processing of the data using various algorithms to
extract the underlying features(Training of the data)
Output
• The final Meaningful data (output) is obtained

4. 1. Machine learning, it’s a branch of Artificial intelligence, which is
basically concerned with the development of algorithms that take
as input empirical data, such as that from sensors or databases.
2. The data obtained is then processed by the algorithm is
designed to identify complex relationships thought to be features
of the underlying mechanism that generated the data, and
employ these identified patterns to make predictions based on
new data.
3. One fundamental difficulty is that the set of all possible behaviors
given all possible inputs is (in most cases of practical interest)
too large to be included in the set of observed examples. Hence
the learner must generalize from the given examples in order to
produce a useful output from new data inputs.

5. It is the process in which an algorithm can
perform accurately on a new data (raw
data) after the training of the data
The training data used is generally of
unknown probability distribution or a bit of
noisy data so the learner has to extract
something informative from the data which
will lead to useful predictions

6. Supervised Learning
Unsupervised Learning
Reinforcement Learning
Semi-supervised learning
Transduction

7.  Supervised learning is the machine learning task
of concluding a function from labeled training data
 This inferred function should predict the correct
output value for any valid input object.
 The training data contains training examples or
training values
 In supervised learning the training data consist of
the input object and the Output object(Supervisory
signal ).
 A supervised learning algorithm analyzes the
training data and produces an inferred
function, which is called a classifier

8. 1. Nature of Training data
2. Build a Training set.
3. Feature selection from the Training set
4. Determine the Structure of learned
Function Eg. SOM, SVM, ANN
5. Test the accuracy of the function.

9.  Unsupervised Learning is used when the data
is unlabeled or we have to find out the hidden
structure.
 Since the examples given to the learner are
unlabeled, there is no error to evaluate a
potential solution.
 This is the distinguishing feature that
separates from supervised learning and
reinforced learning
 Eg. Clustering ,PCA,SOM

10.  This method works on reinforcement from the outside.
The connections among the neurons in the hidden
layer are randomly arranged, then reshuffled as the
network is told how close it is to solving the problem.
 Reinforcement learning is also called supervised
learning, because it requires a teacher. The teacher
may be a training set of data or an observer who
grades the performance of the network results.
 Both unsupervised and reinforcement suffer from
relative slowness and inefficiency relying on a random
shuffling to find the proper connection weights.
 Eg. Genetic Algorithm

11. ANN
1. Artificial neural networks is a type of
learning algorithm that use Neural
network (NN) which are inspired from
biological neurons .
2. It consists of nodes and layers
Input, hidden layer, output layer
3. Each layers are connected by nodes and
specific weights values used for
calculations.

12. Support vector machines (SVMs) are a set
of related supervised learning methods
used for classification and regression.
Its consist of many steps and uses support
vectors ,Hyper planes and feature space

13. 1. Acquisition /Sensing of data(raw sequences )
2. Pre-Processing(remove redundancy)
3. Feature Extraction
4. Feature Selection
5. Classification(Random forest, SVC)
6. Post Processing(Evaluation)
7. Decision

14. Genetic algorithms (GAs) are adaptive
methods which may be used to solve search
and optimization problems. They are based
on the genetic processes Inspired by the
biological evolution process
Uses concepts of “Natural Selection” and
“Genetic Inheritance” (Darwin 1859)
Originally developed by John Holland (1975)

15. Selection, Cross-over,
Mutation
Initial set of
Random solutions
Fitness, Statistics
Stop
Decision
Steps In GA

16. • Character recognition
• Handwriting: processing checks
• Bioinformatics Software's
• Medical Diagnosis
• Credit Card fraud
• Pattern recognition: SNOOPE (bomb detector in
U.S. airports)