Machine Learning EN

Machine Learning: The + Relevant pt. 3

First of all, promises are promises! We continue with this series of articles based on the most relevant and interesting questions about Machine Learning.

Without further ado, let’s get started!

These are some of the most popular machine learning algorithms:

  • Neural networks.
  • Decision trees (Decision Trees).
  • Probabilistic networks.
  • Nearest Neighbor.
  • Support vector machines (Support vector machines).

We can also find some techniques in Machine learning:

What are the different techniques/approaches to algorithms in machine learning?
  • Supervised.
  • Unsupervised.
  • Semi-supervised.
  • By reinforcement.
What is the standard approach to partitioning data in supervised learning?

Certainly the standard approach to supervised learning is to divide the set of examples into the training set and the test set (one can also mention the validation set).

What is the «training set» and the «test set»?

1.First, the training set is a set of examples given to the learner.

2.Finally, the test set is used to test the accuracy of the hypotheses generated by the learner, and is the set of examples that are hidden from the learner.

What is the role of «unsupervised learning»?
  • Finding clusters of data.
  • Get low-dimensional representations of the data.
  • Finding interesting directions in the data.
  • Detect interesting coordinates and correlations.
Examples of the use of ‘Supervised Learning
  • Classifications.
  • Recognition of images.
  • Speech recognition.
  • Regressions.
  • Time series prediction.

Finally, we leave you cordially invited to our last article of this series as interesting as Machine learning and its most relevant questions.

Therefore, we reaffirm that Machine learning is extremely important for LISA Insurtech and the quality of its processes and services. Therefore, we share with you the direct access to our web DEMO, where you can see for yourself the capabilities of our cutting-edge technology.

Machine Learning EN

Machine Learning: Top questions pt. 2

As we saw in the previous article, machine learning has become an opportunity for revolution and innovation in the insurance industry. That is why, as a continuation of this special series of articles, here is part 2.

Shall we begin?

What is a classifier in machine learning?

It is a system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class.

What is a multilayer perceptron (MLP) in machine learning?

It is a kind of feed-forward artificial neural network. An MLP consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer. 

Except for the input nodes, each node is a neuron using a nonlinear activation function. The MLP uses a supervised learning technique called backpropagation for training. 

This is how its multiple layers and nonlinear activation can distinguish data that are not linearly separable.

What is backpropagation?

It is a widely used algorithm for training feedforward neural networks (The pre-feedforward neural network was the first and simplest form of artificial neural network devised), which calculates the gradient of the loss function with respect to the network weights for a single input-output, updating the weights to minimize the loss (gradient descent).

Why use a convolutional neural network instead of an MLP when dealing with images?

Convolutional neural network (CNN): the current favorite of computer vision algorithms: 

  • Weights are smaller (shared and with less waste).
  • Easier to train than MLP (computationally cheaper).
  • It can be deeper, because the layers are sparsely connected rather than fully connected.

What is inductive machine learning?

Inductive machine learning involves the process of learning. For example, where a system, from a set of observed instances, attempts to induce a general rule.

What are the most popular machine learning algorithms and their approaches? And what are test and training sets all about?

Learn about this and much more in our next article!