3. What is Machine Learning?
Arthur Samuel (1959). Machine Learning: Field of study that gives
computers the ability to learn without being explicitly programmed.
Tom Mitchell (1998) Well-posed Learning Problem: A computer program
is said to learn from experience E with respect to some task T and some
performance measure P, if its performance on T, as measured by P,
improves with experience E.
4. Why Machine Learning?
World is so complex, we don't write the explicit solution of every tiny
problem or.
So we need ML for creating Prediction Model.
We need ML for Cluster our unstructured web for structuring search result.
We need ML for creating rational agent who makes decision and gives
maximum output from learning environment.
And list going on..................
6. Supervised learning
Supervised learning is the machine learning task of inferring a function from
labeled training data. The training data consist of a set of training examples.
In supervised learning, each example is a pair consisting of an input object
(typically a vector) and a desired output value (also called the supervisory
signal).
Types:
Classification,
Regression
7. In machine learning and statistics, classification is the
problem of identifying to which of a set of categories (sub-
populations) a new observation belongs, on the basis of a
training set of data containing observations (or instances)
whose category membership is known.
8. Regression is concerned with modelling the relationship
between variables that is iteratively refined using a
measure of error in the predictions made by the model
9. Unsupervised Learning
Unsupervised learning is a type of machine learning algorithm used to draw
inferences from datasets consisting of input data without labeled responses.
The most common unsupervised learning method is cluster analysis, which
is used for exploratory data analysis to find hidden patterns or grouping in
data.
10. Unsupervised Learning Types
Approaches to unsupervised learning include:
clustering. k-means. mixture models. hierarchical clustering,
anomaly detection.
Neural Networks. Hebbian Learning.
Approaches for learning latent variable models such as. Expectation–
maximization algorithm (EM) Method of moments. Blind signal separation
techniques, e.g.
11. Semi-supervised learning
Semi-supervised learning is a class of supervised learning and
unsupervised learning tasks and techniques that also make use of
unlabeled data for training – typically a small amount of labeled data with a
large amount of unlabeled data.
13. Reinforcement Learning
Reinforcement learning is learning what to do--how to map situations to actions--so
as to maximize a numerical reward signal. The machine is not told which actions to
take, as in most forms of machine learning, but instead machine must discover which
actions yield the most reward by trying them. In the most interesting and challenging
cases, actions may affect not only the immediate reward but also the next situation
and, through that, all subsequent rewards. These two characteristics--trial-and-error
search and delayed reward--are the two most important distinguishing features of
reinforcement learning.
