Artificial intelligence is the ability of machines to learn and solve problems like humans. There are two main types of machine learning: supervised learning, where the machine is provided labeled input and output data to learn from, and unsupervised learning, where only unlabeled input data is provided. Supervised learning techniques include regression and classification, while unsupervised techniques include clustering and association. Some applications of AI include medical image analysis, autonomous vehicle control, games, robotic toys, bioinformatics, text analysis and natural language processing.

1. Artificial Intelligence

2. What is Artificial Intelligence ?
 ARTIFICIAL INTELLIGENCE : “The capacity to learn and
solve problems” that is called Artificial Intelligence
 Processes like learning, reasoning, self-correction, etc. are
executed by artificial intelligent machines.
 The capability of a machine to imitate intelligent human
behavior

3. Supervised learning
 In Supervised learning, you train the machine using data
which is well "labeled."
 Supervised learning allows you to collect data or produce a
data output from the previous experience
 For example, Baby can identify other dogs based on past
supervised learning.
 Regression and Classification are two types of supervised
machine learning techniques.
 In a supervised learning model, input and output variables
will be given

4. Unsupervised Learning
 “Unsupervised learning” is a machine learning technique,
where you do not need to supervise the model.
 Unsupervised machine learning helps you to finds all
kind of unknown patterns in dat.
 For example, Baby can identify other dogs based on past
supervised learning.
 Clustering and Association are two types of
Unsupervised learning.
 unsupervised learning model, only input data will be
given

5. Supervised vs. Unsupervised
Learning

6. Parameters Supervised machine
learning technique
Unsupervised machine
learning technique
Process In a supervised learning
model, input and output
variables will be given.
In unsupervised learning
model, only input data will
be given
Input Data Algorithms are trained using
labeled dat.
Algorithms are used
against data which is not
labeled
Algorithms Used Support vector machine,
Neural network, Linear and
logistics regression, random
forest, and Classification
trees.
Unsupervised algorithms
can be divided into
different categories: like
Cluster algorithms, K-
means, Hierarchical
clustering, etc.
Computational
Complexity
Supervised learning is a
simpler method.
Unsupervised learning is
computationally complex
Use of Data Supervised learning model
uses training data to learn a
link between the input and the
outputs.
Unsupervised learning does
not use output dat.
Accuracy of Results Highly accurate and
trustworthy method.
Less accurate and
trustworthy method.
Real Time Learning Learning method takes place
offline.
Learning method takes
place in real time.

7. Number of Classes Number of classes is known. Number of classes is not
known.
Main Drawback Classifying big data can be a
real challenge in Supervised
Learning.
You cannot get precise
information regarding data
sorting, and the output as data
used in unsupervised learning
is labeled and not known.

8. AI Applications
 Medicine:
 Image guided surgery

9. AI Applications
 Medicine:
 Image analysis and enhancement

10. AI Applications
 Transportation:
 Autonomous
vehicle control:

11. AI Applications
 Transportation:
 Pedestrian detection:

12. AI Applications
Games:

13. AI Applications
 Games:

14. AI Applications
 Robotic toys:

15. AI Applications
Other application areas:
 Bioinformatics:
 Gene expression data analysis
 Prediction of protein structure
 Text classification, document sorting:
 Web pages, e-mails
 Articles in the news
 Video, image classification
 Music composition, picture drawing
 Natural Language Processing .
 Perception.