CS 189/289A Introduction to Machine Learning HW2 Solutions University of California, Berkeley COMPSCI 189
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COMPSCI 189
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COMPSCI 189
CS 189/289A Introduction to Machine Learning
Spring 2021 Jonathan Shewchuk HW2: I r Math
Due Wednesday, February 10 at 11:59 pm
• Homework 2 is an entirely written assignment; no coding involved.
• We prefer that you typeset your answers using LATEX or other word processing software. If
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cs 189289a introduction to machine learning spring 2021 jonathan shewchuk hw2 i r math due wednesday
february 10 at 1159 pm • homework 2 is an entirely written assignment no coding in
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CS 189/289A Introduction to Machine Learning
Spring 2021 Jonathan Shewchuk HW2: I r Math
Due Wednesday, February 10 at 11:59 pm
• Homework 2 is an entirely written assignment; no coding involved.
• We prefer that you typeset your answers using LATEX or other word processing software. If
you haven’t yet learned LATEX, one of the crown jewels of computer science, now is a good
time! Neatly handwritten and scanned solutions will also be accepted.
• In all of the questions, show your work, not just the final answer.
• Start early. This is a long assignment. Most of the material is prerequisite material not
covered in lecture; you are responsible for finding resources to understand it.
Deliverables:
1. Submit a PDF of your homework to the Gradescope assignment entitled “HW2 Write-Up”.
You may typeset your homework in LATEX or Word (submit PDF format, not .doc/.docx
format) or submit neatly handwritten and scanned solutions. Please start each question on
a new page. If there are graphs, include those graphs in the correct sections. Do not put
them in an appendix. We need each solution to be self-contained on pages of its own.
• In your write-up, please state whom you had discussions with (not counting course staff)
about the homework contents.
• In your write-up, please copy the following statement and sign your signature next to it.
(Mac Preview and FoxIt PDF Reader, among others, have tools to let you sign a PDF
file.) We want to make it extra clear so that no one inadvertently cheats.
“I certify that all solutions are entirely in my own words and that I have not looked at
another student’s solutions. I have given credit to all external sources I consulted.”
,1 Identities with Expectation
For this exercise, the following identity might be useful: for a probability event A, P(A) = E[1{A}],
where 1{·} is the indicator function.
1. Let X be a random variable with density f (x) = λe−λx 1{x > 0}. Show that E[X k ] = k!
λk
for
integer k ≥ 0. Hint: One way is to do induction on k.
2. For any non-negative random variable X and constant t > 0, show that P(X ≥ t) ≤ E[X]
t
.
Hint: show that for a, b > 0, 1{a ≥ b} ≤ ab .
3. For any non-negative random variable X, prove the identity
Z ∞
E[X] = P(X ≥ t)dt.
0
You may assume that X admits a density to simplify.
4. For any non-negative random variable X with finite variance (i.e., E[X 2 ] < ∞), prove that
(EX)2
P(X > 0) ≥ .
E[X 2 ]
Hint: Use the Cauchy–Schwarz inequality hu, vi2 ≤ hu, uihv, vi. You have most likely seen it
applied when the inner product is the real dot product; however, it holds for arbitrary inner
products. Without proof, use the fact that the expectation E[UV] is a valid inner product of
random variables U and V.
(Note that by assumption we know P(X ≥ 0) = 1, so this inequality is indeed quite powerful.)
5. For a random variable X with finite variance and E[X] = 0, prove that
E[X 2 ]
P(X ≥ t) ≤ for any t ≥ 0
E[X 2 ] + t2
Hint: Try using logic similar to Question 1.4 on t − X.
Solution:
1. Moment Generating Function. Calculate the MGF: MX (t) = E[etX ] = x>0 λe(t−λ)x dx =
R
1
1−(t/λ)
if t < λ and undefined otherwise. Since the MGF is defined in a neighborhood of 0
(specifically |t| < λ), all moments E[X k ] exist. Furthermore, from properties of the MGF,
E[X k ] 1
is the coefficient of tk . Expanding 1−(t/λ) as k≥0 λ1k tk completes the solution.
P
k!
R ∞ case: EX = 1. Inductive hypothesis: for k > 0, EX = λ EX . Inductive
0 k k k−1
Induction. Base
step: EX k = 0 λxk e−λx dx. Let u = xk and dv = λe−λx , so du = kxk−1 and v = −e−λx . Then
R∞ R∞ R∞
0
λxk e−λx dx = [−xk e−λx ]∞
0 + 0 kx e dx = 0 + λk 0 λxk−1 e−λx dx = λk EX k−1 , where the
k−1 −λx
last equality comes from the inductive hypothesis. So EX k = Πki=0 λi = λk!k . Note that the trick
of separating out the k (= kλλ
) factor in the second-to-last equality represents a generally useful
, approach for solving problems: figure out what form you want the problem to “look like”
and try to transform it as close as possible to that form. Since we know we’re dealing with
k−1
induction, we know we would
R ∞ like to somehow obtain EX during the inductive step. By
our assumption, EX k−1 = 0 λxk−1 eλx dx. By keeping this in mind and paying close attention,
we realize we can move a constant λk outside the integral in the second to last equality, leaving
behind the needed λ factor.
[RUBRIC: There could be other ways to solve this. Any completely correct solution gets (+2
points). Any partially correct or incomplete solution gets (+1 point).]
2. When X ≥ t, 1{X ≥ t} = 1 ≤ Xt . On the other hand, when X < t, 1{X ≥ t} = 0 ≤ X
t
since X is
non-negative. Take expectations on both sides to complete.
[RUBRIC: A completely correct solution gets (+1 point).]
3. First see that X = t≥0 1{X ≥ t}dt. Take expectation and use linearity of expectation: E[X] =
R
R R R∞
E t≥0 1{X ≥ t}dt = t≥0 E[1{X ≥ t}]dt = 0 P(X ≥ t)dt. Note that X need not have a density
function for this solution.
Assuming density f (x):
"Z ∞ # Z ∞Z ∞ Z ∞Z ∞
E[X] = E 1{X ≥ t}dt = 1{x ≥ t}dt f (x)dx = 1{x ≥ t} f (x)dx dt
0 0 0 0 0
Z ∞
= P(X ≥ t)dt.
0
Again assuming density f (x):
Z ∞ Z ∞ Z x Z ∞ Z ∞ Z ∞
E[X] = x f (x)dx = f (x)dtdx = f (x)dxdt = P(X ≥ t)dt
0 0 0 0 t 0
[RUBRIC: A completely correct solution gets (+1 point).]
4. Using the non-negativity of X, we have EX = E[X1{X > 0}]. [RUBRIC: (+1 point)]
Now use Cauchy–Schwarz applied to U := X and V := 1{X > 0} to conclude that
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