Machine learning and its importance, Industrial importance of Machine Learning Industrial revolution 4.0 ML and its application Python, Structured, semi-structured, unstructured Evolution of Machine Learning

1. BASICS OF MACHINE LEARNING
AND ITS INDUSTRIAL IMPORTANCE
Prof. Akhilesh Ladha
Lecturer, Information Technology,
R. C. Technical Institute,
Sola, Ahmedabad

2. FLOW OF PRESENTATION
 Definitions
 Learning
 Machine Learning (ML)
 When and Where to use ML
 Evolution of ML
 Learning Process
 Types of Learning
 Industry Application
 Some Paradoxes
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3. EXERCISE-1
WRITE AS MANY WORDS AS YOU CAN READ

4. ANSWER BELOW QUESTIONS
 How many words did you notice/see?
 How many words did you write?
 How many words did you remember more?

5. EXERCISE-1 [REPEAT]
WRITE AS MANY WORDS AS YOU CAN READ

7. DEFINITIONS
 Learning: It is a process by which a system
improves performance from experience.
– Herbert Simon
 Machine Learning: Field of study that gives
computers the ability to learn without being
explicitly programmed.
– Arther Samuel
 Machine Learning: It is the study of algorithms
that improves their performance <P>, at some
task <T> with experience <E>. A well defined
learning task its given by <P,T,E>
– Tom Mitchell

8.  Traditional Programming
 Machine Learning
COMPUTER
INPUT
PROGRAM
OUTPUT
COMPUTER
INPUT
OUTPUT
PROGRAM

9. WHEN AND WHERE TO USE ML
1. Human expertise does not exist
 Mars’ mission
2. Human can’t explain their expertise
 Speech recognition
3. Models can be customized
 Medicines
4. Models are based on huge amount of data
 genomics

10. ML NOT A GOOD OPTION
 Payroll system
 Billing system
 GST calculation
 University CPI/SPI calculation
 and many more...

11. EVOLUTION
Image: Linked In | Machine Learning vs Deep learning

12. KEYPOINTS TO REMEMBER
 Machine Learning works for monotonous task
 As knowledge tuned to specific task
 Is our brain multitask of
monotasked?
Hand rotation exercise

13. WHY ITS IS BEST TIME FOR ML
 System are now flooded with data
 Increase in computational power
 Growing progress is available algorithms and
theory developed by researchers
 Increasing acceptance and support from
industries

14. THE LEARNING PROCESS
https://www.slideshare.net/liorrokach/introduction-to-machine-learning-13809045

15. https://towardsdatascience.com/introduction-to-machine-learning-for-beginners-eed6024fdb08
Types of Learning

16. FIRST LEARNING BRANCH
SUPERVISED
LEARNING
REGRESSION
WEATHER
FORECASTING
Problem solving

17. SECOND LEARNING BRANCH
UNSUPERVISED
LEARNING
CLUSTERING
RECOMMENDER
SYSTEM
Problem solving

18. THIRD LEARNING BRANCH
REINFORCEMENT
REALTIME
DECISION
AUTONOMOUS
CAR
Problem solving

19. 19
PARADOXES AND DILLEMAS
 The Oceam Dillema: In Machine Learning,
accuracy and simplicity (i.e. Interpretability) are
in conflicts
 LESS IS MORE
 Learning from a high-dimensional feature space
requires an enormous amount of training to ensure
that there are several samples with each combination
of values.
 With fixed number of training instances, the
predictive power reduces as the dimensionality
increases. /23

20. BONFERRONI’S PRINCIPLE
 If you look in more places for interesting pattern
than your amount of data will support, you are
bound to find crap.
RHINE PARADOX
 One should not tell other about there ESP
(Extra-Sensory Perception), that will cause them
to lose it.
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21. R
ASHOMON
EFFECT
 The Rashomon effect refers to an
instance when the same event is
described in significantly different
(often contradictory) ways by
different people who were involved.
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23. “TorTure The daTa, and iT wilL confess To anyThing.”
– Ronald Coase
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