Learning

When come to learning, what do we talk about it?

For machines, they are learning from data, since data is what they have.
For our human beings, especially behavioural terms, we are talking about learning from experience.

Machine Learning, is about computers modify or adapt their actions(whether these actions are making predictions or controlling a robot), so that these actions get more accurate, where accuracy is measured by how well the chosen actions reflect the correct ones.

Types of Machine Learning

1. Supervised learning.
   A training set of examples with the correct responses (targets) are provided and, based on this training set, the algorithm generalizes to respond correctly to all possible inputs. This is called learning from examples.
2. Unsupervised learning.
   Correct responses (targets) are not provided, instead the algorithm tries to identify similarities between the inputs so that inputs that have something in common are categorized together. The statistical approach to unsupervised learning is known as density estimation.
3. Reinforcement learning.
   Somewhere between supervised learning and unsupervised learning. The algorithm gets told when the answer is wrong but does not get told how to correct it. It has to explore and try different possibilities until it works out how to get the answer right. It ‘s sometime called learning withe a critic because of this monitor that scores the answer, but does not suggest improvements.
4. Evolutionary learning.
   Biological evolution can be seen as a learning process: biological organisms adapt to improve their survival rates and chances of having offspring in their environment. We’ll look at how we can model this in a computer by using an idea of fitness, which corresponds to a score for how good the current solutions is.

By the way, from ISL(An Introduction to Statistical Learning) by Trevor Hastie and Robert Tibshirani:

Supervised statistical learning involves building a statistical model for predicting, or estimating, an output based on one or more inputs.
With unsupervised statistical learning, there are inputs but no supervising output; nevertheless we can learn relationships and structure from such data.

The most common type of learning is supervised learning.

Supervised

We wish to fit a model that relates the response to the predictors, with the aim of accurately predicting the response for future observations (prediction) or better understanding the relationship between the response and the predictors (inference).

1. Regression
2. Classification
3. GAM
4. boosting
5. support vector machines

Unsupervised

There is no response variable to predict, we observe a vector of measurements $x_i$ but no associated response $y_i$.

1. clustering

Reference:
[1]: Machine Learning: The Complete Guide