Description
Problem 1: Markov Decision Processes (30pts)
1. Consider a Markov decision process with four states s1, s2, s3, s4 and four actions a1, a2, a3, a4
with the following transition and reward functions.
• T(si
, aj ) = sj for all i, j ∈ {1, 2, 3, 4}.
• R(s1, a2) = R(s2, a3) = 1, R(s3, a1) = R(s4, a1) = −1, the rewards of the form
R(si
, ai) = 0 for all states i ∈ {1, 2, 3, 4}, and the rewards for the remaining transitions are equal to .5.
Using the above MDP, answer the following questions.
(a) How many possible deterministic policies are there for this MDP?
(b) For γ = .8, find the optimal value function V
∗ and an optimal policy π
∗
. Is there a
unique optimal deterministic policy?
(c) How does the optimal policy change if γ = .01?
2. For any MDP and any two policies π1 and π2, show how to construct a policy π3 such that
V
π3 (s) ≥ V
π1 (s) for all s ∈ S and V
π3 (s) ≥ V
π2 (s) for all s ∈ S.
3. As we saw in class, Markov decision processes make decisions based on the current state of
the environment and a chosen policy. Suppose that you are given an MDP, but you would
like the agent’s decision to depend on the last two states of the environment instead of just
the last. Can this requirement be formulated in the MDP framework? Explain why or why
not.
Problem 2: Gaussian Mixtures vs. k-means (50pts)
For this problem, you will use the leaf.data file provided with this problem set. This data set
was generated from the UCI Leaf Data Set (follow the link for information about the format of the
data).
The class labels are still in the data set and should be used for evaluation only (i.e., don’t
use them in the clustering procedure), but the specimen number has been removed. You should
preprocess the data so that the non-label attributes have mean zero and variance one.
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1. Train a k-means classifier for each k ∈ {12, 18, 24, 36, 42} starting from twenty different random initializations (sample uniformly from [−3, 3] for each attribute) for each k. Report the
mean and variance of the value of the k-means objective obtained for each k.
2. Train a Gaussian mixture model for each k ∈ {12, 18, 24, 36, 42} starting from twenty different
random initializations (random mean and covariance matrix equal to the identity matrix) for
each k. Report the mean and variance of the converged log-likelihood for each k.
3. Looking at the true labels, for k = 36, which of these two models might you prefer for this
data set?
4. Random initializations can easily get stuck in suboptimal clusterings. An improvement of the
k-means algorithm, known as k-means++, instead chooses an initialization as follows:
(a) Choose a data point uniformly at random to be the first center.
(b) Repeat the following until k centers have been selected:
i. For each data point x compute the distance between x and the nearest cluster center
in the current set of centers. Denote this distance as dx.
ii. Sample a training data point at random from the distribution p such that p(x) ∝ d
2
x
.
Add the sampled point to the current set of centers.
Repeat the first two experiments using this initialization to pick the initial cluster centers for
k-means and the initial cluster means for the Gaussian mixture model. Does this procedure
result in an improvement in either case?
5. Suppose that, instead of allowing the covariance matrix of each mixture component to be an
arbitrary positive definite matrix, we require each covariance matrix to be a diagonal matrix
such that all of its diagonal entries are strictly larger than zero.
Explain how to modify the
EM algorithm and derive the new updates for this special case.
Problem 3: Neural Networks (20pts)
1. Using only perceptrons, construct a neural network for the following input/output pair: Takes
10 binary inputs with one binary output which is 1 if the number of ones in the input is
divisible by four and zero otherwise.
2. Suppose that you add an `2 regularizer, i.e., λ
2
||w||2
2
, to the squared loss minimization problem
for fitting neural networks in class. How does the backpropagation algorithm change?
3. Given a binary classification task, explain the difference between fitting a neural network with
a single perceptron hidden unit to data versus using the perceptron algorithm to fit a linear
classifier to the same data.
Bonus (15pts): Generate a data set for this problem and use MATLAB or Python (you can use
built-in functions for backpropagation) to learn the weights of a sigmoid or relu (e.g., max{0, ·} or
its smooth analog) neural network with the same structure as you produced for the first part of
this question.
You should use a cross entropy loss. How do the learned weights compare to your
perceptron solution as you vary the size of the training set?
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Course Evaluation:
If you haven’t done so already, please go to eval.utdallas.edu and provide feedback on the course.
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