LU 2 Flashcards

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1
Q

What is concept learning

A

Describes the process where experience allows us to partition objects in the world into classes for the purpose of generalization, discrimination and inference

(the process of gaining knowledge or skill by studying practicing or being taught)

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2
Q

what is concept learning in terms of machine learning

A

Teaching machine to distinguish between examples and non- examples of ideas such as symphony, anger etc

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3
Q

Fill in the words
____can be seen as a problem of searching through a predefined space of potential
hypotheses for the hypothesis that best fits the training examples

A

concept learning

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4
Q

in concept learning what does the target concept refer to?

A

The concept or function to be learned (denoted by c (c:x->{0,1})

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5
Q

what is object recognition

A

a computer vision technique for identifying objects in images or videos

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6
Q

What is object detection

A

The process of finding instances of objects in images , a subset of object recognition) objects are also found inside the images

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7
Q

what is a deep learning model that can be used to identify objects?

A

Convolutional neural networks (CNN)

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8
Q

what are the two approaches to performing object recognition using deep learning

A
  1. Training model from scratch by feeding it a large labeled dataset and design a network architecture that learns the features and build the model
  2. use a pretrained deep learning model such as AlexNet or GoogleNet
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9
Q

name an example of machine learning techniques for object recognition

A
  1. HOG feature extraction with an SVM machine learning model
  2. Bag-of-words models with features such as SURF and MSER
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10
Q

how would you perform object recognition using a standard machine learning approach

A

start with a collection of images and select the relevant features in each image

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11
Q

if you have a lot of data and no powerful GPU should you use a machine learning technique or a deep learning technique to train a model

A

machine learning

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12
Q

what is the difference between deep learning and a neural network

A

Deep learning- deep refers to the depth of layers in a neural network - consisting of more than three layers
a neural network has only two or three layers

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13
Q

what are concept feature spaces?

A

the set of all possible values for a chosen set of features from that data.
refers to the collections of features that are used to characterize your data

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14
Q

what is a feature in terms of concept feature spaces?

A

a column or attribute that you’ll use to model your problem excluding the target variable

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15
Q

when is concept feature spaces used?

A

In machine learning (because it is feature extraction)

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16
Q

what does n mean in concept feature spaces?

A

n refers to the number of features

17
Q

Explain what happens to the cases if the features space gets bigger

A

the cases get further apart from each other and there is more empty space between them

18
Q

What is a decision tree

A

It is hierarchical tree structure that consists of root node, branches internal nodes and leaf nodes

19
Q

when do you use a decision tree?( what type of task)

A

– classification
–regression
both

20
Q

what does the nodes in a decision tree represent

A

a test on an attribute

21
Q

what does each leaf node in a decision tree represent

A

classification rules

21
Q

what does a branch in a decision tree represent

A

the outcome of the test

22
Q

Why would you use a decision tree

A

to predict class or value of target variables in supervised learning regression and classification algorithms

23
Q

give some examples of when decision trees are used

A
  • risk management
    healthcare
    capital budgeting
    finance
24
Q

what are some advantages of decision trees

A

easy to interpret (boolean logic)
little to no data preparation required(can handle various data types)
more flexible( both classification as well as regression)

25
Q

what are some disadvantages of decision trees

A

prone to overfitting
high variance estimators
more costly
not fully supported in scikit-learn