Machine Learning and Deep Learning MCQs: Anyone can sharpen their knowledge of machine learning and deep learning with these multiple choice questions and answers.
Multiple choice questions and answers:
Which of the factors affect the performance of learner system does not include? A. Representation scheme used B. Training scenario C. Type of feedback D. Good data structures
What is Machine learning? A. The autonomous acquisition of knowledge through the use of computer programs B. The autonomous acquisition of knowledge through the use of manual programs C. The selective acquisition of knowledge through the use of computer programs D. The selective acquisition of knowledge through the use of manual programs
In language understanding, the levels of knowledge that does not include? A. Phonological B. Syntactic C. Empirical D. Logical
Different learning methods do not include? A. Memorization B. Analogy C. Deduction D. Introduction
A model of language consists of the categories which does not include? A. Language units B. Role structure of units C. System constraints D. Structural units
In language understanding, the levels of knowledge that does not include? A. Syntactic B. Phonological C. Logical D. Empirical
Among the following which is not a horn clause? A. p B. Øp V q C. p → q D. p → Øq
What is a top-down parser? A. Begins by hypothesizing a sentence (the symbol S) and successively predicting lower level constituents until individual preterminal symbols are written B. Begins by hypothesizing a sentence (the symbol S) and successively predicting upper level constituents until individual preterminal symbols are written C. Begins by hypothesizing lower level constituents and successively predicting a sentence (the symbol S) D. Begins by hypothesizing upper level constituents and successively predicting a sentence (the symbol S)
The action ‘STACK(A, B)’ of a robot arm specify to A. Place block B on Block A B. Place blocks A, B on the table in that order C. Place blocks B, A on the table in that order D. Place block A on block B
Which of the following are ML methods? A. based on human supervision B. supervised Learning C. semi-reinforcement Learning D. All of the above
In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called ? A. mini-batches B. optimizedparameters C. hyperparameters D. superparameters
The model will be trained with data in one single batch is known as ? A. Batch learning B. Offline learning C. Both A and B D. None of the above
Different learning methods does not include? A. Introduction B. Analogy C. Deduction D. Memorization
A model of language consists of the categories which does not include ________. A. System Unit B. structural units. C. data units D. empirical units
The action _______ of a robot arm specify to Place block A on block B. A. STACK(A,B) B. LIST(A,B) C. QUEUE(A,B) D. ARRAY(A,B)
p → 0q is not a? A. hack clause B. horn clause C. structural clause D. system clause
Interview type MCQs
1. What is representation in deep learning?
A. It is a way to look at data to represent or encode B. It gets closer to the expected output C. RGB and HSV are two different examples of representations D. All of the above
2. What is learning in deep learning?
A. Learning, in the context of machine learning, describes an automatic search process for better representations B. A process that is “learned” from exposure to known examples of inputs and outputs C. Learning in school attending 5th grade D. Answers A & B E. Answers B & C
3. What is hypothesis space in deep learning?
A. ML/DL algorithms merely searching through a predefined set of operations, called a hypothesis space B. Searching for useful representations of some input data, within a predefined space of possibilities, using guidance from a feedback signal C. Answers A & B D. None of the above
4. What is deep in deep learning?
A. The deep in deep learning is a reference to any kind of deeper understanding achieved by the approach B. It stands for the idea of successive layers of representations in deep learning C. Answers A & B D. None of the above
5. What is depth in deep learning?
A. How many layers contribute to a model of the data is called the depth of the model B. No. of successive layers of representations C. Answers A & B D. None of the above
6. What is shallow learning in deep learning?
A. Machine learning tend to focus on learning only one or two layers of representations of the data B. Machine learning tend to focus on learning 10 layers of representations of the data C. Machine learning tend to focus on learning 512 layers of representations of the data D. Machine learning tend to focus on learning 64 layers of representations of the data
7. What are deep neural networks in deep learning?
A. In deep learning, layered representations are (almost always) learned via models called deep neural networks, structured in literal layers stacked on top of each other B. These networks are the brain neurons studying in neurobiology C. These are models of human brain D. Neural network is a cell in brain
8. Is the statement below true?
The term neural network is a reference to neurobiology, but although some of the central concepts in deep learning were developed in part by drawing inspiration from our understanding of the brain, deep-learning models are not models of the brain. There’s no evidence that the brain implements anything like the learning mechanisms used in modern deep-learning models Answer: Yes Answer: No
9. Which statement is true?
A. Deep learning is a mathematical framework for learning representations from data B. Deep learning is a biological framework for learning representations from brain data C. Deep learning is an analogue framework for learning representations from data D. Deep learning is a digital framework for learning representations from data
10. What are layers in deep learning?
A. Deep neural networks do this input-to-target mapping via a deep sequence of simple data transformations called layers and that these data transformations are learned by exposure to examples B. It is a sort of membrane in brain C. None of the above D. All of the above
11. Find true or false?
The specification of what a layer does to its input data is stored in the layer’s weights, which in essence are a bunch of numbers. In technical terms, we’d say that the transformation implemented by a layer is parameterized by its weights. Weights are also sometimes called the parameters of a layer. A. True B. False
12. Find true or false?
Learning means finding a set of values for the weights of all layers in a network, such that the network will correctly map example inputs to their associated targets A. True B. False
13. Is loss function same as objective function?
A. True B. False
14. What is loss function in deep learning?
A. To control the output of a neural network, you need to be able to measure how far this output is from what you expected B. To calculate loss in banks C. These are true targets of data D. These are the predicted values only
15. Answer true or false as per statement below:
The loss function takes the predictions of the network and the true target (what you wanted the network to output) and computes a distance score, capturing how well the network has done A. True B. False
16. Which statement is true?
A. The fundamental trick in deep learning is to use this score as a feedback signal to adjust the value of the weights a little, in a direction that will lower the loss score B. The fundamental trick in deep learning is to use this score as a feedback signal to adjust the value of the weights a little, in a direction that will higher the loss score C. Both of the above D. None of the above
17. What is training loop in deep learning?
A. With every step in the network processes, the weights are adjusted a little in the correct direction, and the loss score decreases B. It is repeated a sufficient number of times (typically tens of iterations over thousands of examples), yields weight values that minimize the loss function C. All of the above are true D. None of the above are true
18. What is decision boundary in classification problems?
A. A decision boundary can be thought of as a line or surface separating your training data into two spaces corresponding to two categories. B. The data is mapped to a new high-dimensional representation where the decision boundary can be expressed as a hyperplane (if the data was two-dimensional) C. Both of the above are true D. None of the above are true
19. What is kernel function?
A. A kernel function is a computationally tractable operation that maps any two points in your initial space to the distance between these points in your target representation space, completely bypassing the explicit computation of the new representation B. It belongs to classification problems C. All of the above D. None of the above
20. SVM stands for ?
A. Support Vector Machine B. Support Vector Machanism C. Super Visual Machine D. Support Vector Model
21. What are Decision Trees?
A. Decision trees are flowchart-like structures that let you classify input data points or predict output values on given inputs B. These are decision makers for land owners C. All of the above D. None of the above
22. What is Random Forest?
A. Random Forest algorithm introduced a robust, practical take on decision-tree learning that involves building a large number of specialized decision trees and then ensembling their outputs. Random forests are applicable to a wide range of problems B. It is the second-best algorithm for any shallow machine-learning task C. All of the above D. None of the above
23. What are Gradient Boosting Machines?
A. A gradient boosting machine, much like a random forest, is a machine-learning technique based on ensembling weak prediction models, generally decision trees B. It uses gradient boosting, a way to improve any machine-learning model by iteratively training new models that specialize in addressing the weak points of the previous models C. Applied to decision trees, the use of the gradient boosting technique results in models that strictly outperform random forests most of the time, while having similar properties D. All of the above E. None of the above
24. How deep learning learns from data?
A. The incremental, layer-by-layer way in which increasingly complex representations are developed, and the fact that these intermediate incremental representations are learned jointly i.e each layer being updated to follow both the representational needs of the layer above and the needs of the layer below B. Deep learning learns from input data only C. All of the above D. None of the above
25. The two key ideas of deep learning for computer vision:
A. Convolutional neural networks and backpropagation B. Deep neural networks and kernel functions C. Support Vector Machines and loss functions D. None of the above
26. Three technical forces are driving advances in machine learning:
A. Hardware, Datasets & benchmarks, and Algorithmic advances B. Super computers only C. Pen and a piece of paper D. None of the above E. All of the above
27. Overfit model?
A. A model that learns the training dataset too well, performing well on the training dataset but does not perform well on a testing dataset B. A model that fails to sufficiently learn the problem and performs poorly on a training dataset and does not perform well on a testing dataset C. All of above D. None of above
28. Example of reinforcement learning in machine learning?
A. Parking can be achieved by learning automatic parking policies B. Enjoying ice cream sitting in automatic car C. Reinforced learning is the recommendation on Youtube D. Machines are super machines, one can do every task
Answer: A & C