introduction ML

1. BASICS OF MACHINE LEARNING AND
ITS INDUSTRIAL IMPORTANCE
Mrs. Nagarathna C
Asst Prof. CSE, SKIT

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
 Job Opportunities
2 / 19

3. EXERCISE-1
WRITE AS MANY WORDS AS YOU CAN READ

4. ANSWER BELOW QUESTIONS
 How many words did you notice/see?
4/ 19
 How many words did you
write?
 How many words did you remember
more?

5. DEFINITIONS
 Learning: It is a process by which a system
improves performance from experience.
– Herbert Simon
5 / 19
 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

6.  Traditional Programming
COMPUTER
INPUT
PROGRAM OUTPUT
COMPUTER
 Machine Learning
INPUT
OUTPUT
PROGRAM
6/ 19

7. 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
7 / 19

8. EVOLUTION
Image: Linked In | Machine Learning vs Deep learning
8 / 19

9. KEYPOINTS TO REMEMBER
 Machine Learning works for monotonous task
 As knowledge tuned to specific task
 Is our brain multitask of
monotasked?
Hand rotation exercise
9 / 19

10. 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
10 / 19

11. THE LEARNING PROCESS
11 / 19

12. Types of Learning
12 / 19

13. Supervised machine learning
13 / 19

14. Unsupervised machine learning
14 / 19

15. Reinforcement machine learning
In this, the machine learns from a hit and trial method.
Whenever the model predicts or produces a result, it is
penalized if the prediction is wrong or rewarded if the
prediction is correct. Based on these actions the model
trains itself.
15 / 19

16. Applications of Machine
learning
Virtual Personal Assistant
Facial recognition
E-mail spam filter
Recommendation engine on an e-commerce
website
16 / 19

17. Job Opportunities in ML
• Data Scientists
• Data Mining and Analysis
• Machine Learning Engineer
• AI Engineer
• Business Intelligence (BI) Developer
• Robotics programmer
17 / 19

18. 18 / 19

19. “TorTure The daTa, and iT wilL confess To anyThing.”
– Ronald Coase
19/19