Machine Learning Refined
Foundations, Algorithms, and
Applications 2nd Edition By Jeremy
Watt, Reza Borhani, Aggelos
Katsaggelos (Solution Manual)
What is the definition of *learning from
experience* for a computer program? -
<<<ANSWER>>>A computer program is said to
*learn from experience* E with respect to some
*class of tasks* T and *performance measure*
P, if its performance at tasks in T, as measured
by P, improves with experience E.
Which problems can be solved with
unsupervised learning? #2 - <<<ANSWER>>>•
approach problems with little or no idea what
our results should look like.
,• derive structure from data where we don't
necessarily know the effect of the variables.
What is the definition of a *supervised learning
problem*? - <<<ANSWER>>>Given a training
set, learn a function h such that h of an input
variable x is a "good" predictor for the
corresponding output variable y.
What is the definition of a *hypothesis*? -
<<<ANSWER>>>The predicting function h.
Give the *pictorial* process for a supervised
learning problem. - <<<ANSWER>>>Supervised
Learning Problem.
What do we call a learning problem, if the target
variable is continuous? - <<<ANSWER>>>When
the target variable that we're trying to predict is
continuous, the learning problem is also called
a *regression problem*.
What do we call a learning problem, if the target
variable can take on only a small number of
values? - <<<ANSWER>>>When y can take on
only a small number of discrete values, the
,learning problem is also called a *classification
problem*.
How do we measure the *accuracy* of a
hypothesis function? - <<<ANSWER>>>By using
a *cost function*, usually denoted by J.
What is the definition of a *cost function* of a
supervised learning problem? -
<<<ANSWER>>>Takes an average difference of
all the results of the hypothesis with inputs from
x's and the actual output y's.
Give a pictorial representation of what the cost
function of a supervised learning problem does.
- <<<ANSWER>>>Cost function of a supervised
learning problem.
What are alternative terms of a Cost Function?
#2 - <<<ANSWER>>>• *Squared error function*.
• *Mean squared error*.
What is a visual interpretation of the cost
function? #2 - <<<ANSWER>>>• The training
data set is scattered on the X-Y plane.
, • We are trying to make a straight line (defined
by hθ(x)) which passes through these scattered
data points.
What is the *contour line* of a two variable
function? - <<<ANSWER>>>A *contour line* of a
two variable function has a *constant value* at
all points of the same line.
How do we implement an *iteration step* when
calculating Gradient Descent in code? #2 -
<<<ANSWER>>>• At each iteration j, one should
simultaneously update the parameters.
• Updating a specific parameter prior to
calculating another one on the j iteration would
yield to a wrong implementation.
State the algorithm for *gradient descent*. -
<<<ANSWER>>>Repeat until convergence,
where j=0,1 represents the feature index
number.
Depict the graphical implementation of
minimizing the cost function using gradient
descent. #2 - <<<ANSWER>>>• We put theta 0
Foundations, Algorithms, and
Applications 2nd Edition By Jeremy
Watt, Reza Borhani, Aggelos
Katsaggelos (Solution Manual)
What is the definition of *learning from
experience* for a computer program? -
<<<ANSWER>>>A computer program is said to
*learn from experience* E with respect to some
*class of tasks* T and *performance measure*
P, if its performance at tasks in T, as measured
by P, improves with experience E.
Which problems can be solved with
unsupervised learning? #2 - <<<ANSWER>>>•
approach problems with little or no idea what
our results should look like.
,• derive structure from data where we don't
necessarily know the effect of the variables.
What is the definition of a *supervised learning
problem*? - <<<ANSWER>>>Given a training
set, learn a function h such that h of an input
variable x is a "good" predictor for the
corresponding output variable y.
What is the definition of a *hypothesis*? -
<<<ANSWER>>>The predicting function h.
Give the *pictorial* process for a supervised
learning problem. - <<<ANSWER>>>Supervised
Learning Problem.
What do we call a learning problem, if the target
variable is continuous? - <<<ANSWER>>>When
the target variable that we're trying to predict is
continuous, the learning problem is also called
a *regression problem*.
What do we call a learning problem, if the target
variable can take on only a small number of
values? - <<<ANSWER>>>When y can take on
only a small number of discrete values, the
,learning problem is also called a *classification
problem*.
How do we measure the *accuracy* of a
hypothesis function? - <<<ANSWER>>>By using
a *cost function*, usually denoted by J.
What is the definition of a *cost function* of a
supervised learning problem? -
<<<ANSWER>>>Takes an average difference of
all the results of the hypothesis with inputs from
x's and the actual output y's.
Give a pictorial representation of what the cost
function of a supervised learning problem does.
- <<<ANSWER>>>Cost function of a supervised
learning problem.
What are alternative terms of a Cost Function?
#2 - <<<ANSWER>>>• *Squared error function*.
• *Mean squared error*.
What is a visual interpretation of the cost
function? #2 - <<<ANSWER>>>• The training
data set is scattered on the X-Y plane.
, • We are trying to make a straight line (defined
by hθ(x)) which passes through these scattered
data points.
What is the *contour line* of a two variable
function? - <<<ANSWER>>>A *contour line* of a
two variable function has a *constant value* at
all points of the same line.
How do we implement an *iteration step* when
calculating Gradient Descent in code? #2 -
<<<ANSWER>>>• At each iteration j, one should
simultaneously update the parameters.
• Updating a specific parameter prior to
calculating another one on the j iteration would
yield to a wrong implementation.
State the algorithm for *gradient descent*. -
<<<ANSWER>>>Repeat until convergence,
where j=0,1 represents the feature index
number.
Depict the graphical implementation of
minimizing the cost function using gradient
descent. #2 - <<<ANSWER>>>• We put theta 0
