Page 144 - Robotics and AI class 10
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3. Define Machine learning.
Ans. Machine learning is a subset of AI which uses statistical methods to enable machines to improve decision making with
experience. It is one of the most popular techniques to build AI systems across the globe. It is the science of getting machines
to interpret, process and analyse data in order to solve problems. It provides us statistical tools to explore the data.
B. Long answer type questions:
1. Differentiate between Automatic Systems and Autonomous Systems.
Ans. Automatic Systems Automatic systems perform their tasks according to the predefined rules and are deterministic in
nature, meaning that there is a defined problem with the defined steps to solve it. Example is Robotic Arms used to
make cars in the Automobile Industry in the assembly line.
Autonomous Systems Autonomous systems are non-deterministic in nature, meaning they do not have predefined
tasks or defined steps to solve it, as they are trained to learn from their surroundings to act independently. Example is
Self Driven Car.
2. What are the steps involved in Machine learning?
Ans. The Steps in Machine Learning are broadly categorised as follows:
(i) Data Collection: The data collection is the base in the process of Machine Learning.
(ii) Data Preparation and Wrangling: The data collected needs to be prepared and made in a structured manner so
that the correlation between the variables and classes can be understood.
(iii) Model Selection: The Model Selection or Model Building is determined based on the outcome you want to achieve. It
is a build using various analytical techniques of machine learning which are best suited for the task at hand.
(iv) Training the Model: In this process, we use the data prepared for Training and allow the Model Algorithm to
process it and understand the patterns, features and rules, to be able to predict.
(v) Testing, Evaluating and Tuning the Model: The Testing data is used to check the accuracy of the Models
prediction. The evaluation of the results and further Tuning of the algorithm helps the model to achieve complete
accuracy in predicting.
(vi) Deployment and Prediction: Once the model is tested, it is deployed in the real world.
C. Competency-based/Application-based questions: #Digital Literacy
1. Assertion: Data plays a crucial role in AI projects as it serves as the foundation on which the AI model is built and
enables predictions and analysis.
Reasoning: Data is a collection of raw information or facts that are processed to obtain meaningful information. It can
be in various forms, such as text, numbers, audio, and video clips. In AI projects, the model needs to be trained with a
dataset to predict an output accurately, making data an essential part of the AI project.
(i) Both A and R are correct and R is the correct explanation of A.
(ii) Both A and R are correct but R is NOT the correct explanation of A.
(iii) A is correct but R is incorrect.
(iv) A is incorrect but R is correct.
Ans. (i) Both A and R are correct and R is the correct explanation of A.
2. Assertion: A machine is artificially intelligent when it can accomplish tasks by itself.
Reason: Humans become more and more intelligent with time as they gain experiences during their lives.
(i) Both A and R are correct and R is the correct explanation of A.
(ii) Both A and R are correct but R is NOT the correct explanation of A.
(iii) A is correct but R is incorrect.
(iv) A is incorrect but R is correct.
Ans. (i) Both A and R are correct and R is the correct explanation of A.
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