INTRODUCTION TO MACHINE LEARNING Week 11

Session: JULY-DEC 2023

Course Name: Introduction to Machine Learning

Course Link: Click Here

These are Introduction to Machine Learning Week 11 Assignment 11 Answers


Q1. What is the update for πk in EM algorithm for GMM?
a. π(m)k=∑Nn=1γ(znk)|v(m−1)N−1
b. π(m)k=∑Nn=1γ(znk)|v(m)N
c. π(m)k=∑Nn=1γ(znk)|v(m−1)N
d. π(m)k=∑Nn=1γ(znk)|v(m)N−1

Answer: c. π(m)k=∑Nn=1γ(znk)|v(m−1)N


Q2. Consider the two statements:
Statement 1: The EM algorithm can only be used for parameter estimation of mixture models.
Statement 2: The Gaussian Mixture Models used for clustering always outperform k-means and single-link clustering.
Which of these are true?

Both the statements are true
Statement 1 is true, and Statement 2 is false
Statement 1 is false, and Statement 2 is true
Both the statements are false

Answer: Both the statements are false


These are Introduction to Machine Learning Week 11 Assignment 11 Answers


Q3. KNN is a special case of GMM with the following properties: (Select all that apply)
γi=i(2πe)1/2e−12ϵ
Covariance = ϵI
μi=μj∀i,j
πk=1k

Answer: B, D


These are Introduction to Machine Learning Week 11 Assignment 11 Answers


Q4. What does soft clustering mean in GMMs?
There may be samples that are outside of any cluster boundary.
The updates during maximum likelihood are taken in small steps, to guarantee convergence.
It restricts the underlying distribution to be gaussian.
Samples are assigned probabilities of belonging to a cluster.

Answer: Samples are assigned probabilities of belonging to a cluster.


These are Introduction to Machine Learning Week 11 Assignment 11 Answers


Q5. In Gaussian Mixture Models, πi are the mixing coefficients. Select the correct conditions that the mixing coefficients need to satisfy for a valid GMM model.
0≤πi≤1∀i
−1≤πi≤1∀i
∑iπi=1
∑iπi need not be bounded

Answer: A, C


These are Introduction to Machine Learning Week 11 Assignment 11 Answers


Q6. What statement(s) are true about the expectation-maximization (EM) algorithm?
It requires some assumption about the underlying probability distribution.
Comparing to a gradient descent algorithm that optimizes the same objective function as EM, EM may only find a local optima, whereas the gradient descent will always find the global optima
The EM algorithm minimizes a lower bound of the marginal likelihood P(D;θ)
The algorithm assumes that some of the data generated by the probability distribution are not observed.

Answer: A, D


These are Introduction to Machine Learning Week 11 Assignment 11 Answers


Q7. Consider the two statements:
Statement 1: The EM algorithm can get stuck at saddle points.
Statement 2: EM is guaranteed to converge to a point with zero gradient.
Which of these are true?

Both the statements are true
Statement 1 is true, and Statement 2 is false
Statement 1 is false, and Statement 2 is true
Both the statements are false

Answer: Both the statements are true


These are Introduction to Machine Learning Week 11 Assignment 11 Answers

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Session: JAN-APR 2023

Course Name: Introduction to Machine Learning

Course Link: Click Here

These are Introduction to Machine Learning Week 11 Assignment 11 Answers


Q1. Given n samples x1,x2,…,xN drawn independently from an Exponential distribution unknown parameter λ, find the MLE of λ.
a. λMLE=∑ni=1xi
b. λMLE=n∑ni=1xi
c. λMLE=n∑ni=1xi
d. λMLE=∑ni=1xi/n
e. λMLE=n−1/∑ni=1xi
f. λMLE=∑ni=1xi/n−1

Answer: c. λMLE=n∑ni=1xi


Q2. Given n samples x1,x2,…,xn drawn independently from an Geometric distribution unknown parameter p given by pdf Pr(X=k)=(1−p)k−1p for k=1,2,3,⋅⋅⋅ , find the MLE of p.
a. pMLE=∑ni=1xi
b. pMLE=n∑ni=1xi
c. pMLE=n/∑ni=1xi
d. pMLE=∑ni=1xi/n
e. pMLE=n−1/∑ni=1xi
f. pMLE=∑ni=1xi/n−1

Answer: c. pMLE=n/∑ni=1xi


These are Introduction to Machine Learning Week 11 Assignment 11 Answers


Q3. Suppose we are trying to model a p dimensional Gaussian distribution. What is the actual number of independent parameters that need to be estimated in mean and covariance matrix respectively?
a. 1,1
b. p−1,1
c. p,p
d. p,p(p+1)
e. p,p(p+1)/2
f. p,(p+3)/2
g. p−1,p(p+1)
h. p−1,p(p+1)/2+1
i. p−1,(p+3)/2
j. p,p(p+1)−1
k. p,p(p+1)/2−1
l. p,(p+3)/2−1
m. p,p2
n. p,p2/2
o. None of these

Answer: e. p,p(p+1)/2


Q4. Given n samples x1,x2,…,xN drawn independently from a Poisson distribution unknown parameter λ, find the MLE of λ.
a. λMLE=∑ni=1xi
b. λMLE=n∑ni=1xi
c. λMLE=n/∑ni=1xi
d. λMLE=∑ni=1xi/n
e. λMLE=n−1/∑ni=1xi
f. λMLE=∑ni=1xi/n−1

Answer: d. λMLE=∑ni=1xi/n


These are Introduction to Machine Learning Week 11 Assignment 11 Answers


Q5. In Gaussian Mixture Models, πi are the mixing coefficients. Select the correct conditions that the mixing coefficients need to satisfy for a valid GMM model.
a. −1≤πi≤1,∀i
b. 0≤πi≤1,∀i
c. ∑iπi=1
d. ∑iπi need not be bounded

Answer: b, c


Q6. Expectation-Maximization, or the EM algorithm, consists of two steps – E step and the M-step. Using the following notation, select the correct set of equations used at each step of the algorithm.
Notation.
X: Known/Given variables/data
Z: Hidden/Unknown variables
θ: Total set of parameters to be learned
θk: Values of all the parameters after stage k
Q(,): The Q-function as described in the lectures

a. E-step: EZ|X,θ[log(Pr(X,Z|θm))]
b. E-step: EZ|X,θm−1[log(Pr(X,Z|θ))]
c. M-step: argmaxθ∑ZPr(Z|X,θm−2)⋅log(Pr(X,Z|θ))
d. M-step: argmaxθQ(θ,θm−1)
e. M-step: argmaxθQ(θ,θm−2)

Answer: b, d


These are Introduction to Machine Learning Week 11 Assignment 11 Answers

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These are Introduction to Machine Learning Week 11 Assignment 11 Answers
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