UNIT – I
Introduction to Machine Learning – SCSB4009
, UNIT 1 INTRODUCTION TO MACHINE LEARNING
Machine learning-basic concepts in machine learning- types of machine learning-
examples of machine learning- applications- the bias variance- data pre-
processing- noise removal-normalization.
Introduction
Machine Learning is a field of study that gives the computers to Learn Without
Being Explicitly Programmed” "A computer program is said to learn from
experience E with respect to some class of tasks T and performance measure
P, if its performance at tasks in T, as measured by P, improves with
experience E."(Tom Michel)
"Field of study that gives computers the ability to learn without being explicitly
programmed". Learning = Improving with experience at some task
- Improve over task T,
- with respect to performance measure P,
- based on experience
- .E.g., Learn to lay checkers
- T : Play checkers
- P : % of games won in world tournament
- E: opportunity to play against self
Model
, A model of machine learning is a set of programs that can be used to find the
pattern and make a decision from an unseen dataset. It can be any one of the
following
- • Mathematical Equation
- • Relational Diagrams Like Graphs/Trees
- • Logical If/Else Rules
- • Groupings Called Clusters Learning
Training set, Test set and Validation set
• Divide the total dataset into three subsets:
– Training data is used for learning the parameters of the model.
– Validation data is not used of learning but is used for deciding
what type of modeland what amount of regularization works best.
– Test data is used to get a final, unbiased estimate of how well the
network works. We expect this estimate to be worse than on the
validation data.
We could then re-divide the total dataset to get another unbiased estimate of the
true error rate.
DIFFERENCE BETWEEN TRADITIONAL PROGRAMMING VS MACHINE
LEARNING
Introduction to Machine Learning – SCSB4009
, UNIT 1 INTRODUCTION TO MACHINE LEARNING
Machine learning-basic concepts in machine learning- types of machine learning-
examples of machine learning- applications- the bias variance- data pre-
processing- noise removal-normalization.
Introduction
Machine Learning is a field of study that gives the computers to Learn Without
Being Explicitly Programmed” "A computer program is said to learn from
experience E with respect to some class of tasks T and performance measure
P, if its performance at tasks in T, as measured by P, improves with
experience E."(Tom Michel)
"Field of study that gives computers the ability to learn without being explicitly
programmed". Learning = Improving with experience at some task
- Improve over task T,
- with respect to performance measure P,
- based on experience
- .E.g., Learn to lay checkers
- T : Play checkers
- P : % of games won in world tournament
- E: opportunity to play against self
Model
, A model of machine learning is a set of programs that can be used to find the
pattern and make a decision from an unseen dataset. It can be any one of the
following
- • Mathematical Equation
- • Relational Diagrams Like Graphs/Trees
- • Logical If/Else Rules
- • Groupings Called Clusters Learning
Training set, Test set and Validation set
• Divide the total dataset into three subsets:
– Training data is used for learning the parameters of the model.
– Validation data is not used of learning but is used for deciding
what type of modeland what amount of regularization works best.
– Test data is used to get a final, unbiased estimate of how well the
network works. We expect this estimate to be worse than on the
validation data.
We could then re-divide the total dataset to get another unbiased estimate of the
true error rate.
DIFFERENCE BETWEEN TRADITIONAL PROGRAMMING VS MACHINE
LEARNING
