Interview 7

Machine learning

The following interview snippets were given by Fabio Del Frate and cover the definition of AI, the most important aspects of machine learning, the interpretations about deep learning, the topics of supervised and unsupervised learning and its relation with AI as well as the different types of machine learning.

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The interviewee, Fabio Del Frate, is a doctor of computer science, currently employed as a professor at the University of Rome Tor Vergata. He lectures about remote sensing and electromagnetics. In European and Italian Space Agencies he was leading research activity in the field of artificial intelligence applied to satellite data for Earth observations.

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AI is providing machines and computers more degrees of freedom, meaning that machines are asked to go beyond the traditional computer programming. In order for machines to identify patterns, relationships, and build their own knowledge, it is needed to provide them data. Two important elements for interpretation of results in AI are data and models. AI has both positive and inspiring results, but it can also prove to be dangerous. Therefore, researchers must focus on safe development of artificial intelligence. The main principle must be that a human being is always in the role of a supervisor of rules and guidelines, but especially data.

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What is machine learning?

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What does machine learning do?

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What are the results of different types of models?

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Why is it important to evaluate the performance of the model on new data?

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What are crucial factors for successful performance of AI?

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Yes
No
 

Can we say that deep learning has two meanings?

Is deep learning responsible for multiple stages in the process of recognising objects?

Does a mathematical model contain different layers of knowledge?

Is it true that neural networks are not the most used networks for deep learning?

Is one of the meanings of deep learning the topological one?

Neural networks can be shallow or convolutional?

Is deep learning a non-complex strategy?

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True or false?

Supervised learning is not that common sub-branch of machine learning.

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True or false?

Error represents the distance between the decided and actual output values.

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True or false?

The training phase of supervised learning is short.

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True or false?

The end result of supervised learning is very powerful, because it can operate on new data in real time.

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True or false?

Image classification and object detection are examples of supervised learning.

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True or false?

Supervised learning is rarely applied to earth observations.

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True or false?

Biophysical parameters from data collected by satellites are included in supervised learning when earth observations are made.

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1) What are we looking for in data with unsupervised learning? Correlation.
2) Based on what is regrouping of data done in unsupervised learning? Similarities.
3) Are external labelling operations needed in unsupervised learning? No.
4) What is the training of mathematical models based on? Original data.
5) What does the model do with the data at the end of the training in unsupervised learning? It organises it.
6) What do we need to pay attention to in unsupervised learning? Complexity of model.
7) For what in particular can unsupervised learning be used? Dimensionality reduction.

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True or false?

There are only two different machine learning algorithms.

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True or false?

Neural networks algorithms can be deep or shallow.

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True or false?

Support-vector machines and decision trees are algorithms for supervised machine learning..

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True or false?

Self-organising maps, means clustering and principal component analysis are types of supervised machine learning.

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True or false?

The main difference between classification and regression is in the type of output.

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True or false?

Output in classification is a label/class.

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True or false?

Regression estimates a real number.

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True or false?

We can’t transfer the regression problem into a classification one.

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