This document contains most asked question and answers on Machine Learning and Deep Learning to help the students prepare well for the University exams and focus on most important topics.
1. What is Machine Learning and how is ML different from generic AI
applications?
2. What are the basic requirements of ML ? Ans: Sufficient data the includes
all extreme senarios.
3. Demonstrate Classification, Regression and Clustering as the three types
of ML model. (Compare and contrast with suitable examples)
4. Compare Supervised, Unsupervised and Reinforcement learning.
5. Discuss on the loss function or metrics used for evaluating a regression
model.
6. What is confusion matrix? What information can be inferred from a
confusion matrix?
7. State the following: sensitivity, specificity, precision, and accuracy.
8. What is ROC and how can it be used to compare classifiers?
9. Differentiate among linear regression, polynomial regression and logistic
regression.
10. Describe the features used to evaluate ML models.
11. Compare and contrast BNN and ANN
12. Demonstrate different neural network architectures (single layer vs multi
layer, feed forward vs feedback- i.e.RNN, topology mapping nets).
13. Realize logic GATEs (AND, OR, AND, NAND, NOR & Ex-OR) by using
McCulloch Pitt model.
14. Explain efferent vs afferent and excitotary vs inhibitory.
15. Draw and explain perceptron model.
16. What are the limitations of perceptron.
17. State 4-similarity and 4-differences between Perceptron and ADALINE
18. With a neat diagram describe MLP architecture.
,19. Explain the role of hidden layers in MLP.
20. Explain the role of activation functions.
21. Compare vanilla GD, with Batch GD and SGD.
22. A Neural network model has 6 input neurons and 2 output neurons. Along
with the bias how many adjustable parameters does this network contain?
23. Explain the following: overfitting, underfitting, epoch, learning rate,
hyperplane
24. State two differences between supervised learning and unsupervised
learning.
25. For aggregated input is 20 compute the output after sigmoid activation.
, 1. What is Machine Learning and how is ML different from generic AI applications?
Machine Learning is a subset of artificial intelligence which focuses mainly on machine learning
from their experience and making predictions based on its experience.
It enables the computers or the machines to make data-driven decisions rather than being explicitly
programmed for carrying out a certain task. These programs or algorithms are designed in a way
that they learn and improve over time when are exposed to new data.
Artificial Intelligence Machine learning
Artificial intelligence is a technology Machine learning is a subset of AI which allows a
which enables a machine to simulate machine to automatically learn from past data
human behavior. without programming explicitly.
The goal of AI is to make a smart The goal of ML is to allow machines to learn from
computer system like humans to solve data so that they can give accurate output.
complex problems.
In AI, we make intelligent systems to In ML, we teach machines with data to perform a
perform any task like a human. particular task and give an accurate result.
Machine learning and deep learning are Deep learning is a main subset of machine learning.
the two main subsets of AI.
AI has a very wide range of scope. Machine learning has a limited scope.
AI is working to create an intelligent Machine learning is working to create machines that
system which can perform various can perform only those specific tasks for which they
complex tasks. are trained.
AI system is concerned about Machine learning is mainly concerned about
maximizing the chances of success. accuracy and patterns.
The main applications of AI are Siri, The main applications of machine learning
customer support using catboats, are Online recommender system, Google search
Expert System, Online game playing, algorithms, Facebook auto friend tagging
intelligent humanoid robot, etc. suggestions, etc.
On the basis of capabilities, AI can be Machine learning can also be divided into mainly
divided into three types, which three types that are Supervised
are, Weak AI, General AI, and Strong learning, Unsupervised learning,
AI. and Reinforcement learning.
It includes learning, reasoning, and self- It includes learning and self-correction when
correction. introduced with new data.
AI completely deals with Structured, Machine learning deals with Structured and semi-
semi-structured, and unstructured data. structured data.
2. What are the basic requirements of ML ? Ans: Sufficient data the includes all extreme senarios.
, 3. Demonstrate Classification, Regression and Clustering as the three types of ML model.
Machine Learning is broadly classified into Supervised, Unsupervised, Semi-supervised.
Regression and Classification comes under Supervised learning.(answer for all the feature
points are mapped) and Clustering comes under unsupervised learning(answer will not be
given for the points).
Regression - If the prediction value tends to be a continuous value then it falls under
Regression type problem in machine learning
Example : Giving area name, size of land, etc as features and predicting expected cost of the
land.
Classification - If the prediction value tends to be category like yes/no , positive/negative ,
etc then it falls under classification type problem in machine learning
Example : Given a sentence predicting whether it is negative or positive review
Clustering - Grouping a set of points to given number of clusters.
Example : Given 3, 4, 8, 9 and number of clusters to be 2 then the ML system might divide
the given set into cluster 1 - 3, 4 and cluster 2 - 8, 9
Or,
Regression and classification are supervised learning approach that maps an input to an
output based on example input-output pairs, while clustering is a unsupervised learning
approach.
Regression: It predicts continuous valued output.The Regression analysis is the statistical
model which is used to predict the numeric data instead of labels. It can also identify the
distribution trends based on the available data or historic data. Predicting a person’s income
from their age, education is example of regression task.
Classification: It predicts discrete number of values. In classification the data is categorized
under different labels according to some parameters and then the labels are predicted for the
data. Classifying emails as either spam or not spam is example of classification problem.
Clustering: Clustering is the task of partitioning the dataset into groups, called clusters.The
goal is to split up the data in such a way that points within single cluster are very similar and
points in different clusters are different. It determines grouping among unlabeled data.
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