ASU CSE 598 Intro to Deep Learning in Visual Computing
Graded Quiz Week 2
1. In a linear regression model, the price of a house was found to
depend on 2 features x1 - number of floors, x2 - number of bedrooms.
If the parameters of the linear regression model are θ = [θ0 , θ1 , θ2 ] =
[−1.83, 3, 4]T . What is the price of a house in the same area that has
5 floors and 4 bedrooms?
2. If the labels for 3 data points (x(1) = 1, x(2) = 2, x(3) = 3) are
(y (1) = 5.5, y (2) = 6.5, y (3) = 7.5) respectively, estimate the parameters
of a linear regression model, θ = [θ0 , θ1 ]T using Least Squares closed
form?
3. Identify the Univariate Gaussian distribution X ∼ N (µ, σ 2 ) that
best fits the data. Data: x(1) = 2, x(2) = 0, x(3) = −1, x(4) = 1.
4. In a span of m days, it rained for n days (n < m). If P(rain)=θ,
what is the best θ if rain on a given day does not depend on rain
on a previous day?
1
5. The activation of logistic neuron is defined by F (x) = 1+e−x , what
is the derivative ∂F
∂x ?
...
Find the rest of the questions and their solutions with detailed explanations
on the next page !
Graded Quiz Week 2
1. In a linear regression model, the price of a house was found to
depend on 2 features x1 - number of floors, x2 - number of bedrooms.
If the parameters of the linear regression model are θ = [θ0 , θ1 , θ2 ] =
[−1.83, 3, 4]T . What is the price of a house in the same area that has
5 floors and 4 bedrooms?
2. If the labels for 3 data points (x(1) = 1, x(2) = 2, x(3) = 3) are
(y (1) = 5.5, y (2) = 6.5, y (3) = 7.5) respectively, estimate the parameters
of a linear regression model, θ = [θ0 , θ1 ]T using Least Squares closed
form?
3. Identify the Univariate Gaussian distribution X ∼ N (µ, σ 2 ) that
best fits the data. Data: x(1) = 2, x(2) = 0, x(3) = −1, x(4) = 1.
4. In a span of m days, it rained for n days (n < m). If P(rain)=θ,
what is the best θ if rain on a given day does not depend on rain
on a previous day?
1
5. The activation of logistic neuron is defined by F (x) = 1+e−x , what
is the derivative ∂F
∂x ?
...
Find the rest of the questions and their solutions with detailed explanations
on the next page !
