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Machine Learning Concepts
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1
Question
Which knowledge level is not inclduded in language understanding? A. Empirical B. Logical C. Phonological D. Syntactic
Answer
Empirical
2
Question
Concept learning infers a ____-valued function from examples. A. Decimal B. Hexadecimal C. Boolean D. All of the above
Answer
Boolean
3
Question
Which is not a supervised learning method? A. Naive Bayesian B. PCA C. Linear Regression D. Decision Tree Answer
Answer
PCA
4
Question
Applications like real-time decisions and robot navigation are primarily associated with? A. Supervised Learning: Classification B. Reinforcement Learning C. Unsupervised Learning: Clustering D. Unsupervised Learning: Regression
Answer
Reinforcement learning
5
Question
Targeted marketing and customer segmentation are examples of which learning?
Answer
Unsupervised learning: Clustering
6
Question
Fraud detection and image classification are applications of?
Answer
Supervised learning: Classification
7
Question
Which does not function as a symbolic representation in ML? A. Supervised Learning: Classification B. Unsupervised Learning: Clustering C. Unsupervised Learning: Regression D. Reinforcement Learning
Answer
Hidden-Markov Models (HMM)
8
Question
FIND-S algorithm generalizes hypothesis based on? A. Negative B. Positive C. Negative or Positive D. None of the above
Answer
Positive examples
9
Question
FIND-S ignores? A. Negative B. Positive C. Both D. None of the above
Answer
Negative examples
10
Question
The Candidate-Elimination Algorithm represents? A. Solution Space B. Version Space C. Elimination Space D. All of the above
Answer
Version space
11
Question
Inductive learning is based on the idea that frequent occurrences suggest?
Answer
It is likely to be generally true
12
Question
A drawback of FIND-S is that it assumes training set consistency? True or False?
Answer
True
13
Question
Strategies to reduce overfitting in decision trees include?
Answer
Enforcing max depth, minimum samples in leaves, pruning
14
Question
Which is a widely used bagging-based ML algorithm? A. Decision Tree B. Random Forest C. Regression D. Classification
Answer
Ans. B. Random Forest
15
Question
To find extrema, the gradient of a function is set to zero because?
Answer
The gradient at extrema is always zero
16
Question
Disadvantage of decision trees? A. Decision trees are robust to outliers B. Decision trees are prone to be overfit C. None of the above
Answer
B. Prone to overfit
17
Question
Perceptron is? A. A single layer feed-forward neural network with pre-processing B. A neural network that contains feedback C. A double layer auto-associative neural network D. An auto-associative neural network
Answer
A. A single layer feed-forward neural network
18
Question
Neural networks have training time depending on?
Answer
Size of the network and data
19
Question
Advantages of neural networks include? A. They have the ability to learn by B. They are more fault C. They are more suited for real time operation due to their high computational power D. All of above
Answer
D. All (Learning ability, fault tolerance, real-time suitability)
20
Question
What is Neuro software?
Answer
Powerful and easy neural network software
21
Question
Which is true for neural networks? A. Each node computes it‟s weighted input B. Node could be in excited state or non-excited state C. It has set of nodes and connections D. All of the above
Answer
Ans. D: All (NN's each node computes weighted inputs, can be excited or not, and nodes are interconnected)
22
Question
Objective of backpropagation? A. To develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly B. To develop learning algorithm for multilayer feedforward neural network C. To develop learning algorithm for single layer feedforward neural network D. All of the above
Answer
Ans: A: Train multilayer networks to capture input-output mappings
23
Question
Single-layer neural networks cannot?
Answer
Perform pattern recognition and parity detection
24
Question
Backpropagation is also called?
Answer
Generalized delta rule
25
Question
Neural networks generally?
Answer
Have higher computational rates than traditional computers
26
Question
What is true regarding backpropagation rule? A. Error in output is propagated backwards only to determine weight updates B. There is no feedback of signal at any stage C. It is also called generalized delta rule D. All of the above
Answer
Ans D. All of the above
27
Question
There is feedback in final stage of backpropagation A. True B. False
Answer
False, There is NO feedback in final stage of backpropagation
28
Question
A 3-input neuron has weights 1, 4 and 3. The transfer function is linear with the constant of proportionality being equal to 3. The inputs are 4, 8 and 5 respectively. What will be the output? A. 139 B. 153 C. 162 D. 160
Answer
153 Net input = (w₁ × x₁) + (w₂ × x₂) + (w₃ × x₃) Net input = (1 × 4) + (4 × 8) + (3 × 5) Net input = 4 + 32 + 15 = 51 Since the transfer function is linear with proportionality constant = 3: Output = 3 × Net input= 3 × 51 = 153
29
Question
In backpropagation, hidden layer outputs?
Answer
Are computed for error calculation
30
Question
Backpropagation involves?
Answer
Error transmission and weight adjustment