The document provides a history of machine learning, describing several important developments: - In the 1950s, Arthur Samuel developed one of the first machine learning programs to play checkers and coined the term "machine learning." - In 1957, Frank Rosenblatt proposed the perceptron, an early type of neural network. - In 1969, Marvin Minsky showed the limitations of perceptrons in handling certain data distributions. This led to a decline in neural network research until the 1980s. - In 1986, John Ross Quinlan developed decision tree learning and the ID3 algorithm, which became popular tools in machine learning.

1. Introduction
UNIT I
BTCOC503 : Machine Learning

2. What is Learning?
• Learning is an ability to improve behavior and
knowledge based on experience.
BTCOC503 : Machine Learning

3. Machine Learning History
• 1950s:
– Arthur Samual Developed a program for
playing checkers.
– The program was able o observe the
positions of the game and learn a model that
gives better moves for the machine player.
– 1959: Samual Coined the term “Machine
Learning”
– Samual Defined machine learning as ”A field
of studies that gives computers the ability to
learn without being explicitly programmed”
BTCOC503 : Machine Learning

4. Machine Learning History
• 1957:
– Rosenblatt proposed the perceptron
– Perception is a simple neural network
unit.
– “Perceptron is designed to illustrate
some of the fundamental properties
of intelligent systems in general”
– Developed Linear classifier
BTCOC503 : Machine Learning

5. Machine Learning History
• 1969:
– Minsky ..Came up with Limitations of
perceptron.
– He showed that X-or problem could not be
represented by perceptron and such
inseparable data distribution cannot be
handled.
– And due to this up to 1980 the research on
Neural network get dormant
BTCOC503 : Machine Learning

6. Machine Learning History
• 1986:
– John Ross Quinlan came up with
decision tree learning ,specially
the Iterative Dichotomiser 3 ID3
algorithm.
– It was also released as a software and it
has very simplistic rules contrary to the
black box of neural network, which got
popular.
– After ID3 very interesting concepts were
developed.
– During this ‘type symbolic natural
language’ processing also become very
popular.
BTCOC503 : Machine Learning

7. Machine Learning History
BTCOC503 : Machine Learning
Neural
network Back
propagation
network

8. Machine Learning History
BTCOC503 : Machine Learning

9. Traditional Programming
Machine Learning
Computer
Data
Program
Output
BTCOC503 : Machine Learning
Computer
Data
Output
Program
/ Model

10. What is Machine Learning?
• Machine Learning is a concept which allows
the machine to learn from examples and
experience, and that too without being
explicitly programmed.
• It Explores algorithm-
– To learn and build the models for data
– Models are used for prediction ,decision making ,
solving problems etc.
BTCOC503 : Machine Learning

11. What is Machine Learning?
• Machine Learning is a concept which allows
the machine to learn from examples and
experience, and that too without being
explicitly programmed.
• It Explores algorithm-
– To learn and build the models for data
– Models are used for prediction ,decision making ,
solving problems etc.
BTCOC503 : Machine Learning

12. How Does SIRI work?
??????????

14. Type of Machine Learning?
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Semi-Supervised Learning

16. Supervised Learning
• The Model is able to Predict with the help of
labelled dataset.
• For every instance there is a corresponding
output.
• Somewhat like Learning with Teacher..
• What is labelled dataset?
– Data for which you already know the Target
answer is called as labelled dataset.

17. Supervised Learning

18. Supervised Learning

19. Types of Supervised Learning
• Classification
–Discrete valued Y
–Machine is trained to classify something into
some class
–Ex. Patient has disease or not
–Ex. Email is spam or not

20. Supervised Learning: Classification

21. Types of Supervised Learning
• Regression
– Continuous valued Y
– Machine is trained to predict some value like
price, weight, or height.

22. Supervised Learning: Classification

23. Unsupervised Learning

29. Classification Learning
• Task T:
– Input
– Output
• Performance metric P
• Experience E

33. How do you get data for the Machine Learning Problem?

34. How do you get data for the Machine Learning Problem?

38. Hypothesis Space
• Set of candidate outputs that you can get.
• In Supervised learning there is set of
functions that comprises hypothesis space
and you want to find out that function from
the hypothesis space which is most probable
given your training examples