# ECE1513H – Assignment 1 solution

\$30.00

Category:

## Description

Rate this product

Problem 1 Assume we collected a dataset D = {(Xi
, Yi)}i∈1..7 of N = 7 points (i.e.,
observations) with inputs X = (1, 2, 3, 4, 5, 6, 7) and outputs Y = (6, 4, 2, 1, 3, 6, 10) for a
regression problem.
1. (0 points) Draw a scatter plot of the dataset on a spreadsheet software (e.g., Excel).
2. (6 points) Let us use a linear regression model gw,b(x) = wx + b to model this data.
Write down the analytical expression of the mean squared loss of this model on dataset
D. Your loss should take the form of
1
2N
X
i∈1..N
Aw2 + Bb2 + Cwb + Dw + Eb + F
where A, B, C, D, E, and F are expressed only as a function of Xi and Yi and constants.
Do not fill-in any numerical values yet.
3. (3 points) Derive the analytical expressions of w and b by minimizing the mean squared
loss from the previous question. Your expressions for parameters w and b should only
depend on A, B, C, D and E. Do not fill-in any numerical values yet.
4. (1 point) Give approximate numerical values for w and b by plugging in numerical values
from the dataset D.
5. (0 points) Double-check your solution with the scatter plot from the question earlier:
e.g., you can use Excel to find numerical values of w and b.
Problem 2 Let us now assume that D is a dataset with d features per input and N > 0
inputs. We have D = {((Xij )j∈1..d, Yi)}i∈1..N . In other words, each Xi
is a column vector
with d components indexed by j such that Xij is the jth component of Xi
. The output Yi
remains a scalar (real value).
Let us assume for simplicity that b = 0 so we have a simplified linear regression model:
g ~w(X) = ~wX
where ~w is now a vector of dimensionality d. Each component of ~w multiplies the corresponding feature of X, which gives the following: g ~w(Xi) = P
j∈1..d wjXij .
ECE1513H – Winter 2020 Assignment 1 – Page 2 of 2 Due Jan 20th
We would like to train a regularized linear regression model, where the mean squared loss is
augmented with an `2 regularization penalty 1
2
k~wk
2
2
on the weight parameter ~w:
L( ~w, D) = 1
2N
X
i∈1..N
(Yi − g ~w(Xi))2 +
λ
2
k~wk
2
2
where λ > 0 is a hyperparameter that controls how much importance is given to the penalty.
1. (2 points) Let A =
P
i∈1..N XiX>
i
. Give a simple analytical expression for the components of A.
2. (3 points) Let us write B =
P
i∈1..N YiXi
, prove that the following holds:
∇L( ~w, D) = 1
N
(A ~w − B) + λ ~w
3. (1 point) Write down the matrix equation that ~w

should satisfy, where:
~w
∗ = arg min
~w
L( ~w, D)
Your equation should only involve A, B, λ, N, and ~w

.
4. (2 points) Prove that all eigenvalues of A are positive.
5. (2 points) Demonstrate that matrix A + λNId is invertible by proving that none of its
eigenvalues are zero. Here, Id is the identity matrix of dimension d.
6. (2 points) Using the invertibility of matrix A+λNId, solve the equation stated in question 3 and deduce an analytical solution for ~w

. You’ve obtained a linear regression
model regularized with an `2 penalty.

∗ ∗