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A detailed diagram illustrating Intelligent Vision Systems with elements like cameras, data processing, and AI algorithms, bright and visually appealing, focusing on technology and machine learning

Intelligent Vision Systems Quiz

Test your knowledge on Intelligent Vision Systems and machine learning concepts with our engaging quiz! Whether you're a student or a professional, this quiz will help you reinforce your understanding of key principles.

Topics covered include:

Data leakage scenarios
Deep learning performance metrics
Classifier properties
AI regulation in the EU

15 Questions4 MinutesCreated by AnalyzingData42

Referring to the class discussion on data leakage what is the worst situation?

The unwanted leakage of data from training dataset to test data set

The unwanted leakage of data from test dataset to training data set since you are subtracting data to the generalization test, making the situation more optimistic

None of the other options since transferring data from test and/or training dataset is normal when the accuracy of the model is tested

The unwanted leakage of data from test dataset to training data set since you are subtracting data to the generalization test, making the situation more pessimistic

According to the class discussion, considering the training of deep learning models on standard CPUs and standard commercial GPUs boards, what is the gain in training performance (time) and efficiency (energy) for a medium/large-size project?

About 100x in performance and 10x in efficiency

More than 100x in performance and more than 5x in efficiency

About 10x in performance and 5x in efficiency

About 2x in performance and 2x in efficiency

A basic industrial setup for Intelligent vision systems is typically composed by the following elements:

Standard industrial camera with optics, processing HW and SW units, illumination system

Standard industrial smart camera with optics, external processing HW and SW units, illumination system

Standard industrial camera with optics, illumination system

None of the other options

According to the class discussion, what is the classifier with the following properties: - not based on neural techniques; - it’s deterministic with no random initialization; - perfect repeatability; - a minimum number of parameters is needed; learning is very simple but effective; - perfect explain ability

KNN

Linear classifier

Decision Tree

K-means

None of the other options

According to the class discussion, what kind of activity can be performed on the test set?

Mean test error estimation

Mean test error estimation and stardad deviation

All the answer

Confusion matrix test

The following activity: a) Data Selection; b) Data Filtering; c) Data Enhancing…

Are part of the classical machine learning approaches and they are (corretly) no longer used in deep learning applications

Contribute to keep lower the complexity of the learning task

Normal acrivities & lower complexity

Are part of the jon of the artificial intellient specialist in normale activities

According to the class discussion the kNN classifier, what kind of learning is it?

Instance-based Learning

Hard-limited Learning

None of the answer

Eager Learning

Unsupervised Clustering

An AI model is processing an input RGB image to evaluate the age expressed in year of the face present in the image. What kind of the model is it?

Classifier Model

Regressior Model

Clustering Model

Reinforced Learning Model

Non of the Above

According to the class discrussion, the theory of Intelligent Systems should incloude the folloquing designing steps

Representation

Representation, evaluation

Represtentations, evaluations, optimization

Non of the other options

Clustering alway requires a supervised dataset

True

False

According to the class discussion, using a blackbox solution is

Bad practice for a ML designer

Can be used under specific circumstances

Sinse all state of the art moels tend to be quite large and unexplainable, it is current good practice to adopt the black box approach since tou get the best model

According to the class discussion to the classifications systems and their decision boundaries, it is possibile uin general to optimize during the training/optimization step

The accuracy

The margin

Both

According to the class discussion about AI regulation in EU, the regualtion approach is based

Lost of use cases

Risk assessment of the application

Both

None of the ather options

According to the discussion presented in class, the EU regulatory framework for AI is:

Mainly focused on public services

Mainly focused on healt-related applications

Mainly focus on data privacy

Non of the other options

Considering the possible Intelligent Vision tasks which is the correct option?

Instance Segmentation is more complex than Object Detection

. Instance Segmentation is less complex than Object Detection

Instance Segmentation and Object Detection have a similar complexity

The other options are not Intelligent Vision tasks

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