MACHINE LEARNING CT |COMPLETE STUDY GUIDE WITH
100% ACCURATE QUESTIONS & ANSWERS | ACE EVERY
TEST | GUARANTEED EXCELLENCE.
Well-posed Learning Problem Answer: A computer program is said to learn from (E)xperience
E with respect to some (T)ask T and some (P)erformance measure P, if its performance on T, as
measured by P, improves with experience E.
Machine Learning Answer: Field of study that gives computers the ability to learn without
being explicitly programmed.
Machine Learning broad definition Answer: Field of study that gives computers the ability to
learn without being explicitly programmed.
Abstract Essence of ML Answer: Representation + Evaluation + Optimisation
Machine Learning Answer: Learning from experience. It's also called supervised learning,
were experience E is the supervision.
Pattern Recognition Answer: Finding patterns without experience. It's also called
unsupervised learning.
Unsupervised Learing Answer: - We only have xi values, but no explicit target labels.
- You want to do 'something' with them.
Unsupervised Learning Tasks Answer: - Outlier detection: Is this a 'normal' xi ?
- Data visualization: What does the high-dimensional X look like?
- Association rules: Which xij occur together?
- Latent-factors: What 'parts' are the xi made from?
- Ranking: Which are the most important xi ?
- Clustering: What types of xi are there?
Classification Answer: ML task where T has a discrete set of outcomes. Often classification is
binary.
Examples:
• face detection
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,• smile detection
• spam classification
• hot/cold
Regression Answer: ML task where T has a real-valued outcome on some continuous sub-
space
Examples:
• Age estimation
• Stock value prediction
• Temperature prediction
• Energy consumption prediction
Labels Answer: Values that h aims to predict
Example:
• Facial expressions of pain
• Impact of diet on astronauts in space
• Predictions of house prices
Training algorithm Answer: Given a model h with Solution Space S and a training set {X,Y},
a learning algorithm finds the solution that minimizes the cost function J(S)
Features/Attributes Answer: Measurable values of variables that correlate with the label y
Examples:
• Sender domain in spam detection
• Mouth corner location in smile detection
• Temperature in forest fire prediction
• Pixel value in face detection
Cost Function Answer: Squared error cost function. J(S)
Local minima Answer: The smallest value of the function. But it might not be the only one.
General classification problem Answer: If classes are disjoint, i.e. each pattern belongs to one
and only one class then input space is divided into decision regions separated by decision
boundaries or surfaces
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,Decision surfaces are Answer: • Linear functions of x
• Defined by (D-1) dimensional hyperplanes in the D dimensional input space.
Linear Separability Answer: Linearly separable data:
• Datasets whose classes can be separated by linear decision surfaces
• Implies no class-overlap
• Classes can be divided by e.g. lines for 2D data or planes in 3D data
Orthogonality Answer: - Two vectors and are orthogonal if they're perpendicular
- If their inner product is 0:
a·b=0
LDA Answer: - Linear Discriminant Analysis
- Most commonly used as dimensionality reduction technique in the pre-processing step for
pattern-classification and machine learning applications.
- The goal is to project a dataset onto a lower-dimensional space with good class-separability
in order avoid overfitting
Training LDA objective: Answer: Find (i.e. learn) that minimizes some error function on the
training set.
Significant approaches:
• Least squares
• Fisher
• Perceptron
Artificial Neural Nets Answer: • Feed-forward neural network/Multilayer Perceptron one of
many ANNs
• We focus on the Multilayer Perceptron
• Really multiple layers of logistic regression models
The simplest ANNs consist of Answer: • A layer of D input nodes
• A layer of hidden nodes
• A layer of output nodes
• Fully connected between layers
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, Curse of Dimensionality Answer: The curse of dimensionality refers to how certain learning
algorithms may perform poorly in high-dimensional data.
First, it's very easy to overfit the the training data, since we can have a lot of assumptions that
describe the target label (in case of supervised learning). In other words we can easily express
the target using the dimensions that we have.
Second,we may need to increase the number of training data exponentially, to overcome the
curse of dimensionality and that may not be feasible.
Third, in ML learning algorithms that depends on the distance, like k-means for clustering or k
nearest neighbors, everything can become far from each others and it's difficult to interpret the
distance between the data points.
EXTREME Dimensionality Case Answer: In an extreme, degenerate case, if D > n, each
example can be uniquely described by a set of feature values.
Hidden layer(s) can Answer: - Have arbitrary number of nodes/units
- Have arbitrary number of links from input nodes and to output nodes (or to next hidden layer)
- There can be multiple hidden layers
Hidden Unit Activation Answer: Common functions for are unit step, sigmoid or logistic and
tanh
RELU Answer: Rectified Linear Unit
New trend, responsible for great deal of Deep Learning success. Advantages:
• No 'vanishing gradient' problem
• Can model any positive real value
• Can stimulate sparseness
Output layer can be Answer: • Single node for binary classification
• Single node for regression
• n nodes for multi-class classification
Network Topology Answer: Variations include:
• Arbitrary number of layers
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100% ACCURATE QUESTIONS & ANSWERS | ACE EVERY
TEST | GUARANTEED EXCELLENCE.
Well-posed Learning Problem Answer: A computer program is said to learn from (E)xperience
E with respect to some (T)ask T and some (P)erformance measure P, if its performance on T, as
measured by P, improves with experience E.
Machine Learning Answer: Field of study that gives computers the ability to learn without
being explicitly programmed.
Machine Learning broad definition Answer: Field of study that gives computers the ability to
learn without being explicitly programmed.
Abstract Essence of ML Answer: Representation + Evaluation + Optimisation
Machine Learning Answer: Learning from experience. It's also called supervised learning,
were experience E is the supervision.
Pattern Recognition Answer: Finding patterns without experience. It's also called
unsupervised learning.
Unsupervised Learing Answer: - We only have xi values, but no explicit target labels.
- You want to do 'something' with them.
Unsupervised Learning Tasks Answer: - Outlier detection: Is this a 'normal' xi ?
- Data visualization: What does the high-dimensional X look like?
- Association rules: Which xij occur together?
- Latent-factors: What 'parts' are the xi made from?
- Ranking: Which are the most important xi ?
- Clustering: What types of xi are there?
Classification Answer: ML task where T has a discrete set of outcomes. Often classification is
binary.
Examples:
• face detection
APPHIA – Crafted with Care and Precision for Academic Excellence.
1
,• smile detection
• spam classification
• hot/cold
Regression Answer: ML task where T has a real-valued outcome on some continuous sub-
space
Examples:
• Age estimation
• Stock value prediction
• Temperature prediction
• Energy consumption prediction
Labels Answer: Values that h aims to predict
Example:
• Facial expressions of pain
• Impact of diet on astronauts in space
• Predictions of house prices
Training algorithm Answer: Given a model h with Solution Space S and a training set {X,Y},
a learning algorithm finds the solution that minimizes the cost function J(S)
Features/Attributes Answer: Measurable values of variables that correlate with the label y
Examples:
• Sender domain in spam detection
• Mouth corner location in smile detection
• Temperature in forest fire prediction
• Pixel value in face detection
Cost Function Answer: Squared error cost function. J(S)
Local minima Answer: The smallest value of the function. But it might not be the only one.
General classification problem Answer: If classes are disjoint, i.e. each pattern belongs to one
and only one class then input space is divided into decision regions separated by decision
boundaries or surfaces
APPHIA – Crafted with Care and Precision for Academic Excellence.
2
,Decision surfaces are Answer: • Linear functions of x
• Defined by (D-1) dimensional hyperplanes in the D dimensional input space.
Linear Separability Answer: Linearly separable data:
• Datasets whose classes can be separated by linear decision surfaces
• Implies no class-overlap
• Classes can be divided by e.g. lines for 2D data or planes in 3D data
Orthogonality Answer: - Two vectors and are orthogonal if they're perpendicular
- If their inner product is 0:
a·b=0
LDA Answer: - Linear Discriminant Analysis
- Most commonly used as dimensionality reduction technique in the pre-processing step for
pattern-classification and machine learning applications.
- The goal is to project a dataset onto a lower-dimensional space with good class-separability
in order avoid overfitting
Training LDA objective: Answer: Find (i.e. learn) that minimizes some error function on the
training set.
Significant approaches:
• Least squares
• Fisher
• Perceptron
Artificial Neural Nets Answer: • Feed-forward neural network/Multilayer Perceptron one of
many ANNs
• We focus on the Multilayer Perceptron
• Really multiple layers of logistic regression models
The simplest ANNs consist of Answer: • A layer of D input nodes
• A layer of hidden nodes
• A layer of output nodes
• Fully connected between layers
APPHIA – Crafted with Care and Precision for Academic Excellence.
3
, Curse of Dimensionality Answer: The curse of dimensionality refers to how certain learning
algorithms may perform poorly in high-dimensional data.
First, it's very easy to overfit the the training data, since we can have a lot of assumptions that
describe the target label (in case of supervised learning). In other words we can easily express
the target using the dimensions that we have.
Second,we may need to increase the number of training data exponentially, to overcome the
curse of dimensionality and that may not be feasible.
Third, in ML learning algorithms that depends on the distance, like k-means for clustering or k
nearest neighbors, everything can become far from each others and it's difficult to interpret the
distance between the data points.
EXTREME Dimensionality Case Answer: In an extreme, degenerate case, if D > n, each
example can be uniquely described by a set of feature values.
Hidden layer(s) can Answer: - Have arbitrary number of nodes/units
- Have arbitrary number of links from input nodes and to output nodes (or to next hidden layer)
- There can be multiple hidden layers
Hidden Unit Activation Answer: Common functions for are unit step, sigmoid or logistic and
tanh
RELU Answer: Rectified Linear Unit
New trend, responsible for great deal of Deep Learning success. Advantages:
• No 'vanishing gradient' problem
• Can model any positive real value
• Can stimulate sparseness
Output layer can be Answer: • Single node for binary classification
• Single node for regression
• n nodes for multi-class classification
Network Topology Answer: Variations include:
• Arbitrary number of layers
APPHIA – Crafted with Care and Precision for Academic Excellence.
4
