Machine Learning Fundamentals Questions and Answers

Q: The weight w for the autoassociative BAM set of vectors \((x_{1}, x_{2},....., x_{L})\) is computed as ........................... .

The key gives (A) \(\sum x_{i}x_{i}^{t}\).An autoassociative bidirectional associative memory (BAM) stores each pattern against itself. Its weight matrix is the sum of the outer products of the stored vectors, \(W = \sum_{i=1}^{L} x_{i}x_{i}^{t}\). With each \(x_{i}\) a column vector of \(n\) elements, \(x_{i}x_{i}^{t}\) is an \(n \times n\) matrix, so W is \(n \times n\) and can recall a stored pattern from a noisy copy.Option B gives the same matrix if the vectors are written as rows, so it depends on a convention that the question does not state, but with the usual column-vector reading it is the scalar \(x_{i}^{t}x_{i}\) and cannot be a weight matrix. Options C and D divide one vector by another, which is not defined for vectors.

11.

The weight w for the autoassociative BAM set of vectors \((x_{1}, x_{2},....., x_{L})\) is computed as ........................... .

A.\(\sum_{i=1}^{L} x_{i}\,x_{i}^{t}\)
B.\(\sum_{i=1}^{L} x_{i}^{t}\,x_{i}\)
C.\(\sum_{i=1}^{L} \left(\frac{x_{i}}{x_{i}^{t}}\right)\)
D.\(\sum_{i=1}^{L} \left(\frac{x_{i}^{t}}{x_{i}}\right)\)

Exam Year(s)

MH SET - 2025, SET Exam - 2025,

Answer: A

The key gives (A) \(\sum x_{i}x_{i}^{t}\).

An autoassociative bidirectional associative memory (BAM) stores each pattern against itself. Its weight matrix is the sum of the outer products of the stored vectors, \(W = \sum_{i=1}^{L} x_{i}x_{i}^{t}\). With each \(x_{i}\) a column vector of \(n\) elements, \(x_{i}x_{i}^{t}\) is an \(n \times n\) matrix, so W is \(n \times n\) and can recall a stored pattern from a noisy copy.

Option B gives the same matrix if the vectors are written as rows, so it depends on a convention that the question does not state, but with the usual column-vector reading it is the scalar \(x_{i}^{t}x_{i}\) and cannot be a weight matrix. Options C and D divide one vector by another, which is not defined for vectors.

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12.

Match the following :

List - I	List - II
a. Supervised learning	1. The decision system receives rewards for its action at the end of a sequence of steps.
b. Unsupervised learning	2. Manual labels of inputs are not used.
c. Reinforcement learning	3. Manual labels of inputs are used.
d. Inductive learning	4. System learns by example

Codes : a b c d

A.1 2 3 4
B.2 3 1 4
C.3 2 4 1
D.3 2 1 4

Exam Year(s)

UGC NET - 2012,

Answer: D

Supervised learning uses manual labels of the inputs (3), unsupervised learning does not use them (2), reinforcement learning is learning in which the decision system gets rewards for its action at the end of a sequence of steps (1), and inductive learning is learning by example (4). The code is 3 2 1 4. The other codes swap the first two, which is the main distinction between supervised and unsupervised learning, or give the rewards to inductive learning. The keyed answer is the fourth code.

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