1. Define Machine Learning.
Machine learning is a branch of artificial intelligence that develops algorithms by learning
the hidden patterns of the datasets used it to make predictions on new similar type data, without
being explicitly programmed for each task..
2. Recall the Applications of Machine learning.
Traffic Alerts.
Social Media.
Transportation and Commuting.
Products Recommendations.
Virtual Personal Assistants.
3. Identify the various issues in Machine learning.
Inadequate Training Data
Poor quality of data
Non-representative training data
Monitoring and maintenance
Over fitting and Under fitting
4. Outline the concept learning as a search.
Searching through a large space of hypotheses implicitly defined by the
hypothesis representation (same for more general learning).
The hypothesis representation defines the space of hypotheses the program can
ever represent and therefore can ever learn.
5. Name the four modules of final design in checkers learning problem.
The performance System
The Critic
The General
The Experiment Generatorizer
6. Interpret the perspective on machine learning.
The goal in the machine learning is to recognize the pattern in the dataset, in
general manner.
After you recognize the patterns, you can use this information to model the data, to interpret
, the data, or to predict the outcome of the new data which hasn't seen before.
7. State the inductive Learning Hypothesis.
Hypothesis is a Boolean-valued function defined over a large set of training data
The inductive learning hypothesis states that any hypothesis found to approximate the target
function well over a sufficiently large set of training examples will also approximate the
target function well over other unobserved examples.\
8. List out the algorithms of concept learning.
FIND-S Algorithm
Candidate Elimination Algorithm
List-Then-Eliminate Algorithm
9. Generalize the concept of Biased Hypothesis Space
The inductive bias (also known as learning bias) of a learning algorithm is the set of
assumptions that the learner uses to predict outputs.
In machine learning, one aim to construct algorithms that are able to learn to predict a
certain target output. Inductive Bias = Y=a+bx (Linear Model) HYPOTHESIS SPACE
10. Define version space.
A version space is a hierarchical representation of knowledge that enables you to keep track
of all the useful information supplied by a sequence of learning examples without
remembering any of the examples.
11. How version space is different from hypothesis space?
Instance Space: It is a subset of all possible example or instance.
Version Space: The Version Space denotes VSHD (with respect to hypothesis space H and
training example D) is the subset of hypothesis from H consistent with training example in
D.
red: Generalization of Hypothesis. green: Specification of hypothesis.
12. Identify the instances for the Enjoy Sport concept learning task.
Sky – (values: Sunny, Cloudy, Rainy)
AirTemp – (values: Warm, Cold)
Humidity – (values: Normal, High)
Wind – (values: Strong, Weak)
, Water – (values: Warm, Cold)
Forecast – (values: Same, Change)
13. Examine how we use the more-general-than partial ordering to organize the search for a
hypothesis consistent with the observed training examples
The Find-S algorithm illustrates one way in which the more general than partial ordering can be
used to organize the search for an acceptable hypothesis.
It searches from the most specific to progressively more general hypotheses along one chain of
the partial ordering.
14. Label the set of instance with example.
A single object of the world from which a model will be learned, or on which a model will be
used (e.g., for prediction).
In most machine learning work, instances are described by feature vectors; some work uses
more complex representations (e.g., containing relations between instances or between parts of
instances).
15 .Identify the key properties of Find-S Algorithm.
FIND-S is guaranteed to output the most specific hypothesis within H that is consistent with the
positive training examples
FIND-S algorithm’s final hypothesis will also be consistent with the negative examples
provided the correct target concept is contained in H, and provided the training examples are
correct.
16. Differentiate FIND-S and Candidate Elimination Algorithm.
FIND-S outputs a hypothesis from H, that is consistent with the training examples, this is just
one of many hypotheses from H that might fit the training data equally well.
The key idea in the Candidate-Elimination algorithm is to output a description of the set of all
hypotheses consistent with the training examples.
Machine learning is a branch of artificial intelligence that develops algorithms by learning
the hidden patterns of the datasets used it to make predictions on new similar type data, without
being explicitly programmed for each task..
2. Recall the Applications of Machine learning.
Traffic Alerts.
Social Media.
Transportation and Commuting.
Products Recommendations.
Virtual Personal Assistants.
3. Identify the various issues in Machine learning.
Inadequate Training Data
Poor quality of data
Non-representative training data
Monitoring and maintenance
Over fitting and Under fitting
4. Outline the concept learning as a search.
Searching through a large space of hypotheses implicitly defined by the
hypothesis representation (same for more general learning).
The hypothesis representation defines the space of hypotheses the program can
ever represent and therefore can ever learn.
5. Name the four modules of final design in checkers learning problem.
The performance System
The Critic
The General
The Experiment Generatorizer
6. Interpret the perspective on machine learning.
The goal in the machine learning is to recognize the pattern in the dataset, in
general manner.
After you recognize the patterns, you can use this information to model the data, to interpret
, the data, or to predict the outcome of the new data which hasn't seen before.
7. State the inductive Learning Hypothesis.
Hypothesis is a Boolean-valued function defined over a large set of training data
The inductive learning hypothesis states that any hypothesis found to approximate the target
function well over a sufficiently large set of training examples will also approximate the
target function well over other unobserved examples.\
8. List out the algorithms of concept learning.
FIND-S Algorithm
Candidate Elimination Algorithm
List-Then-Eliminate Algorithm
9. Generalize the concept of Biased Hypothesis Space
The inductive bias (also known as learning bias) of a learning algorithm is the set of
assumptions that the learner uses to predict outputs.
In machine learning, one aim to construct algorithms that are able to learn to predict a
certain target output. Inductive Bias = Y=a+bx (Linear Model) HYPOTHESIS SPACE
10. Define version space.
A version space is a hierarchical representation of knowledge that enables you to keep track
of all the useful information supplied by a sequence of learning examples without
remembering any of the examples.
11. How version space is different from hypothesis space?
Instance Space: It is a subset of all possible example or instance.
Version Space: The Version Space denotes VSHD (with respect to hypothesis space H and
training example D) is the subset of hypothesis from H consistent with training example in
D.
red: Generalization of Hypothesis. green: Specification of hypothesis.
12. Identify the instances for the Enjoy Sport concept learning task.
Sky – (values: Sunny, Cloudy, Rainy)
AirTemp – (values: Warm, Cold)
Humidity – (values: Normal, High)
Wind – (values: Strong, Weak)
, Water – (values: Warm, Cold)
Forecast – (values: Same, Change)
13. Examine how we use the more-general-than partial ordering to organize the search for a
hypothesis consistent with the observed training examples
The Find-S algorithm illustrates one way in which the more general than partial ordering can be
used to organize the search for an acceptable hypothesis.
It searches from the most specific to progressively more general hypotheses along one chain of
the partial ordering.
14. Label the set of instance with example.
A single object of the world from which a model will be learned, or on which a model will be
used (e.g., for prediction).
In most machine learning work, instances are described by feature vectors; some work uses
more complex representations (e.g., containing relations between instances or between parts of
instances).
15 .Identify the key properties of Find-S Algorithm.
FIND-S is guaranteed to output the most specific hypothesis within H that is consistent with the
positive training examples
FIND-S algorithm’s final hypothesis will also be consistent with the negative examples
provided the correct target concept is contained in H, and provided the training examples are
correct.
16. Differentiate FIND-S and Candidate Elimination Algorithm.
FIND-S outputs a hypothesis from H, that is consistent with the training examples, this is just
one of many hypotheses from H that might fit the training data equally well.
The key idea in the Candidate-Elimination algorithm is to output a description of the set of all
hypotheses consistent with the training examples.
