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Pennsylvania State University
College of Information Sciences and Technology
DS 310 Midterm Exam
Instructions
1. There are 4 problems each of which is worth 25 points.
2. Please consult the instructor if you have difficulty understanding any of the problems.
1. (25 pts.) Consider a M-class pattern classification problem in which each pattern
X to be classified belongs to exactly one of M mutually exclusive classes ω1 ··· ωM.
Suppose that X is represented using a vector of N binary features X = [x1, x2 ··· xN ].
Let pji = P(xi = 1|ωj ). Assume that the features are independent given the class
label. Let P(ω1)··· P(ωM) be the prior probabilities of each class.
(a) Describe in words, the meaning of pji.
(b) Show that a classifier that assigns X to class ωk gk(X) ≥ gj (X)∀j ̸= k. where
gi(X) can be written in the form !N
i=1 wjixi + wj0 is a minimum error (Bayes
optimal) classifier. Express wji, in terms of pji and P(ωj ) where (1 ≤ j ≤ M;
0 ≤ i ≤ N).
(c) Generalize the above solution for (b) above to obtain minimum loss classification
where λij is the loss incurred in assigning a pattern to class ωi when it in fact
belongs to class ωj .
2. (25 pts.) Suppose you have been hired by an AI consulting firm. Indicate which
algorithm you would choose in each of the following data-driven knowledge acquisition
scenarios. In each case, briefly justify your recommendation.
(a) Your client, has a database of patient records containing symptoms and expert
diagnosis. She would like to build a diagnosis system. The attributes can be
numeric (e.g., patient’s temperature), as well as categorical (e.g., whether the
patient is pregnant). In addition to performing accurate diagnosis of patients, your
client would like to use the system to obtain insight regarding the relationships
between attributes for different diseases.
(b) Your client is designing an email organizer to be integrated into an email program.
The SPAM detector is to be trained using information that is obtained
by observing the user’s actions on each email (read and respond, mark as junk,
ignore).
(c) Your client is a large company which has managed to gather a large database of
information about past applicants to jobs in various categories (e.g., software engineer,
data scientist, etc.) and the hiring decisions on each applicant. Experts in
the organization are convinced that they can automate the process of shortlisting
applicants for further consideration. You are told that it is important that the
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decision-making process be transparent – that is, it should be easy to understand
why an applicant was shortlisted (or not).
(d) Your client is a large hospital that is interested in reducing the workload on
hospital has a large database of radiological images labeled with expert diagnosis.
The goal here is to automate diagnosis on the cases where the AI system can
produce high confidence results, and identify a subset of cases that the AI system
3. (25 pts.) Recall that the perceptron learning algorithm that was described in class
is an additive weight update algorithm - that is, we add or subtract a fraction of
the misclassified sample to the weight vector at each iteration. Consider instead,
a multiplicative weight update algorithm for an n-input neuron defined as follows:
Consider a neuron defined by two weight vectors w+ and w−. Suppose both weight
vectors are initialized with a value 1 for each of their components. Consider a training
example (xp, dp) where xp ∈ {−1, 1}n is an input pattern and dp ∈ {−1, 1} is its
class label. Let yp, the output of the classifier be 1 if w+ · xp > w− · xp and yp =
−1 otherwise. Suppose the weights are updated as follows: w+
i ← w+
i β−(dp−yp)xip
and w−
i ← w−
i β(dp−yp)xip where 0 < β < 1 is a learning rate. Comment on the
potential advantages of such a multiplicative weight update algorithm over its additive
counterpart. Prove that this algorithm is guaranteed to converge to a pair of weight
vectors (w+⋆ , w−⋆ ) that correctly classify the training data whenever such weight vectors
exist. Hint: Show that the multiplicative weight update algorithm as an instance of
the standard perceptron algorithm operating on a suitably transformed weight space.
4. (a) (12.5 pts. )
Suppose you have designed (or trained) a n-input perceptron with the weight
vector W and threshold θ to correctly classify a set S of n-dimensional patterns.
Further assume that all the weight values of the perceptron so designed have
been hard-wired but the threshold θ can be set under user control. When the
perceptron is later used on a factory floor, suppose you find that the source of
the patterns (say the camera or the digitizer) adds a fixed amount of noise (given
by the n-dimensional noise vector V) to each pattern. Assuming that you can
somehow measure (or can calculate) V, how would you change θ so that the
perceptron with the same weight vector W continues to correctly classify all the
patterns in S?
(b) (12.5 pts) Briefly explain the significance of the following properties of the error
functions used to derive gradient-descent based learning algorithms of the type
considered in part (a).
i. Continuity and Differentiablilty (with respect to the weights or other parameters
of interest)
ii. Convexity with respect to weights or other parameters of interest

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