2. GROUP D13-3
(INSTRUMENTATION)
WE WILL BE PRESENTING THE FOLLOWING:
• INTRODUCTION TO MACHINE LEARNING
• THE BASICS OF MACHINE LEARNING
• APPLICATIONS OF MACHINE LEARNING IN INDUSTRY
o PRODUCT CATEGORIZATION
o IMPROVING ACCURACY OF INERTIAL MEASUREMENT
UNIT USING SUPERVISED MACHINE LEARNING
o DATA MINING TECHNIQUES
o MACHINE LEARNING FOR MEDICAL DIAGNOSIS
• FUTURE SCOPE OF MACHINE LEARNING

3. MACHINE
LEARNING

4. DEFINITION OF MACHINE LEARNING
Ability of a machine to improve its own
performance through the use of a software
that employs artificial intelligence
techniques to mimic the ways by which
humans seem to learn such as repetition
and experience.

5.  Helps in building machines exhibiting
intelligent behavior.
 Apart from artificial intelligence it is also
used in administration, commerce and
industry.
 The most widely known demonstration of
this migration is ‘DATA MINING’.

6.  Makes human-computer interaction easier
 Relatively simple to integrate
 Will distinguish your products from others
 Increase customer satisfaction
 Will improve simple-intelligent systems

7.  Medical diagnosis
 Data mining
 Bioinformatics
 Speech and handwriting recognition
 Product categorization
 Inertial measurement unit (IMU)
 Information retrieval

8. CONCEPT OF
MACHINE LEARNING

9. What exactly is
“Machine Learning”??
Input Output
Database
+
Set of Rules
(sensors) (Predictions)
BLACKBOX

10. • Machine learning, a branch of artificial intelligence, is a
scientific discipline concerned with the design and
development of algorithms.
• Computer system (expert system) which is imbued with
decision making ability like a human expert.
• Two parts: a. Knowledge base (database)
b. Inference engine (predicted with certain
probability)
• It is a learner (like a small baby) which looks at the
examples (obstacles), analyses it using stored data
which also includes previous experiences, finds its
algorithm and predicts the possible solution with highest
probability.
• It keeps updating itself with every obstacle
solved, enhancing its performance every time.
• Its algorithms includes different combinations of logic.

11. Necessity of Human Machine
Interfacing
•
• Number of types of obstacles in real
world are huge, hence it has to proceed
to a generalize solution using certain
set of rules.
• Only disadvantage being its high error-
making.
• And thus human interfacing with these
systems becomes necessary.

12. So How Does These Expert
Systems Differ from Humans...??

14. Need for Product
Categorization

15. Difference in the Scale of
Companies

16. So How Does it Work ...???

17. Features of Product
Categorization
• Classification
• Filtering
• Suggestions / history
• User personal information

18. IMU SYSTEM USING
SUPERVISED MACHINE
LEARNING

19. ANGLE MEASUREMENT

20. Motion Capture Technology

21. Disadvantages of Motion Capture
• Specific hardware and special software
increase cost
• Camera field of view is necessary
• More space required
• No calibrations or manipulation while
recording data
• Sometimes we require more than one
camera for accuracy
• needs proper lightning conditions

22. So What Should We Do...??

23. Inertial Measurement Unit (IMU)
system with Machine Learning

24. Inertial Measurement Unit (IMU)
with Machine Learning
• IMU is sensor which measures acceleration
and angular velocity rate.
• The inertial measurement unit using support
vector regression method has the
advantages of having a small size as well as
quite low cost.
• As compared to the motion capture
system, it provides better positional data
analysis.

25. Enhancement in Accuracy of IMU
Kernel trick
-Inner product space
-Linear analysis
Support vector machine
-Classification
-Regression analysis

26. Data Mining

27. What is Data Mining?
• Intersection of computer science and
statistics
• Data mining software is analytical tools for
analyzing data.
• Data mining is process of finding correlations
or patterns among large databases.

28. What is a 'Pattern'?
It is the probability of distribution of similar data.
Or in other words its just a relation between the
variables.
Machine learning and Data Mining:
1. The computer sorts the data based
on the algorithm.
2. If there is some drastic change in data then,
the algorithm tries to find relation between
them and adapts accordingly.

29. Identifying non-trivial, valid and useful patterns in a
given database is known as
'Knowledge Discovery in Databases (KDD)'.
Steps:
• Understand and define problem.
• Extract Data:
We should extract data what we need from the
Database.
• Data Engineering:
Deal with missing variables, rescale data,
Combine similar attributes.
• Algorithm Engineering (This is the ML part):
Figure out what algorithm to use or write one.

30. An Overview of the Knowledge
Discovery in Database (KDD) Process

31. Applications :
• DM for Artificial Neural Networks :
In most cases a neural network is an adaptive system
that changes its structure so Data Mining is used to
model complex relationships between inputs and outputs
or to find patterns in data.
• Instance-based Learning Algorithms for
DM :
Instance-Based Learning (IBL) is defined as the
generalizing of a new instance to be classified from the
stored training examples, which is widely used for
classification tasks.Here actually the machine learn from
the experience.

32. Me
Machine learning for
Medical Diagnosis
Medical Diagnosis
by Machine Learning

33. • Medical diagnosis:
It is a procedure to identify disorder in a
person.
• Machine learning for medical
diagnosis:
It means that the computer will identify the
symptoms and tell what that particular person is
diagnosed with.

34. SURVEY OF DIAGNOSIS BY
ALGORITHMS/ PHYSICIANS

35. SELECTION OF THE APPROPRIATE
MACHINE LEARNING SYSTEM
 Good performance
 The transparency of diagnostic knowledge.
 The ability to explain decisions
 The ability of the algorithm to reduce the
number of tests necessary to obtain reliable
diagnosis.
 The ability to appropriately deal with
missing data.

36. MACHINE LEARNING ALGORITHMS
 1. STATISTICAL OR PATTERN
RECOGNITION.
 2. INDUCTIVE LEARNING OF
SYMBOLIC RULES.
 3. ARTIFICIAL NEURAL NETWORKS.

37. Medical Imaging:
•Medical Imaging is taking photos of body
parts (both internal and external) and
analyzing them for a disorder.
•CCD and GDV are types of image devices
which have found great applications in
Machine Learning Systems.

39. CONCLUSION
 IN FUTURE, THE STUDY OF MACHINE LEARNING
HOLDS EXCITING PROSPECTS WITH CONSTANT
INNOVATIONS IN DIVERSE FIELDS.
 WITH BETTER ALGORITHMS, WE CAN
COMPLETELY BRIDGE THE GAP BETWEEN MEN
AND MACHINES.
 BECAUSE IT IS A TYPE OF ADAPTIVE LEARNING, IT
WILL FIND APPLICATIONS IN ALL POSSIBLE
FIELDS.

40. THANK YOU!