Machine Learning Algorithms Quiz

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Questions and Answers

Which of the following is NOT a type of machine learning algorithm mentioned in the text?

  • Clustering
  • Reinforcement Learning
  • Dimensionality Reduction
  • Supervised Learning (correct)

Dimensionality reduction techniques aim to make data easier to visualize.

True (A)

What is the primary goal of clustering algorithms?

To group similar data points together based on patterns or trends identified within the dataset.

In reinforcement learning, the model learns through trial and error using feedback in the form of ______ and ______.

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What type of dataset is used in a supervised learning model?

<p>Labeled dataset (C)</p> Signup and view all the answers

Regression models in supervised learning work with discrete data.

<p>False (B)</p> Signup and view all the answers

What are labels used for in supervised learning?

<p>Labels categorize data.</p> Signup and view all the answers

In unsupervised learning, the model works on __________ datasets.

<p>unlabeled</p> Signup and view all the answers

Match the following terms with their definitions:

<p>Classification = Works on labeled data Regression = Predicts continuous outcomes Unsupervised Learning = Works on unlabeled data Patterns = Identifiable structures within data</p> Signup and view all the answers

Which of the following is an example of a supervised learning model?

<p>Predicting next salary (A)</p> Signup and view all the answers

Which of the following is NOT a stage of the AI Project cycle?

<p>Data Mining (B)</p> Signup and view all the answers

The Sustainable Development Goals (SDGs) aim to end poverty, protect the planet, and ensure peace and prosperity for all people.

<p>True (A)</p> Signup and view all the answers

Unsupervised learning helps in identifying relationships and patterns in data.

<p>True (A)</p> Signup and view all the answers

Name the 4Ws of problem canvases in the problem scoping stage.

<p>Who, what, where, why</p> Signup and view all the answers

What is a primary goal of unsupervised learning?

<p>Identify patterns in data.</p> Signup and view all the answers

The two different approaches for AI modeling are _______ Based and Learning Based.

<p>Rule</p> Signup and view all the answers

What is a characteristic of a Rule Based model?

<p>It follows predefined rules set by the developer. (D)</p> Signup and view all the answers

In the Learning Based approach, the model does not modify itself according to changes in data.

<p>False (B)</p> Signup and view all the answers

What happens to a machine once it is trained under the Rule Based approach?

<p>It does not adapt to changes in the training dataset.</p> Signup and view all the answers

Match the AI modeling approaches with their definitions.

<p>Rule Based = Follows instructions set by the developer Learning Based = Adapts and modifies itself based on new data</p> Signup and view all the answers

Which of the following is NOT a source for collecting reliable datasets?

<p>Social Media Posts (A)</p> Signup and view all the answers

Problem scoping is the final step in the AI project cycle.

<p>False (B)</p> Signup and view all the answers

What are the four parameters used to evaluate an AI model?

<p>Acquisition, Precision, Recall, F1 Score</p> Signup and view all the answers

The process of acquiring data for an AI project is known as ______.

<p>Data Acquisition</p> Signup and view all the answers

Match the following stages in the AI project cycle with their descriptions:

<p>Problem Scoping = Setting the goal for the AI project Data Exploration = Visualizing data patterns Data Modelling = Selecting and testing models Evaluation = Assessing model efficiency</p> Signup and view all the answers

Which step comes immediately after Data Acquisition in the AI project cycle?

<p>Data Exploration (C)</p> Signup and view all the answers

The 4Ws problem canvas helps in analyzing the people affected by a problem.

<p>True (A)</p> Signup and view all the answers

What is the main purpose of Data Modelling in the AI project cycle?

<p>To decide upon and test suitable models for the project</p> Signup and view all the answers

Which of the following accurately describes stakeholders in a problem?

<p>People who face the problem and would benefit from a solution. (A)</p> Signup and view all the answers

Artificial Neural Networks require programmers to manually feed input features.

<p>False (B)</p> Signup and view all the answers

What is a key characteristic of every node in an Artificial Neural Network?

<p>It is a machine learning algorithm.</p> Signup and view all the answers

In rule-based AI modeling, the machine follows the rules defined by the ______.

<p>developer</p> Signup and view all the answers

Match the following AI modeling approaches with their characteristics:

<p>Rule-Based Approach = Patterns defined by the developer Learning-Based Approach = Automatically learns from data Artificial Neural Network = Modeled on the human brain Feature Extraction = Automatic feature discovery</p> Signup and view all the answers

What context should you consider when analyzing a problem?

<p>The situation and locations where the problem arises. (D)</p> Signup and view all the answers

Artificial Neural Networks are only useful for small datasets.

<p>False (B)</p> Signup and view all the answers

What is the primary difference between rule-based and learning-based AI?

<p>Rule-based uses predefined rules while learning-based automatically learns from data.</p> Signup and view all the answers

What is the primary focus of a Learning Based Approach in AI?

<p>Allowing the machine to learn patterns from unlabeled data (B)</p> Signup and view all the answers

An Artificial Neural Network consists of only one layer.

<p>False (B)</p> Signup and view all the answers

What is the purpose of the Hidden Layer in an Artificial Neural Network?

<p>To perform computations that lead to the output.</p> Signup and view all the answers

The goal of evaluation in an AI project cycle is to understand the ______ of the AI model.

<p>reliability</p> Signup and view all the answers

Which of the following best describes an Input Layer in an Artificial Neural Network?

<p>The first layer that accepts inputs from the programmer (A)</p> Signup and view all the answers

What does the Problem Statement Template help summarize?

<p>Key points of a problem.</p> Signup and view all the answers

Evaluation in AI is only necessary before deploying a model.

<p>False (B)</p> Signup and view all the answers

_______ learning techniques enable an agent to learn from its own actions and experiences in an interactive environment.

<p>Reinforcement</p> Signup and view all the answers

Dimensionality reduction techniques aim to decrease the number of dimensions in data.

<p>True (A)</p> Signup and view all the answers

Which of the following is NOT a characteristic of the data exploration stage?

<p>Developing new algorithms for data analysis. (B)</p> Signup and view all the answers

Match the machine learning algorithms with their primary purpose:

<p>Clustering = Grouping similar data points together Dimensionality Reduction = Simplifying high-dimensional data Reinforcement Learning = Learning through trial and error with rewards and punishments</p> Signup and view all the answers

What is the main aim of clustering algorithms?

<p>Clustering algorithms are used to group similar data points together based on patterns or trends.</p> Signup and view all the answers

Which of these is NOT a source for collecting reliable datasets?

<p>Personal opinions and beliefs (D)</p> Signup and view all the answers

The data exploration stage focuses on acquiring new data sources.

<p>False (B)</p> Signup and view all the answers

Data can be defined as a collection of ______ or ______ collected for reference and analysis.

<p>information</p> Signup and view all the answers

Flashcards

Supervised Learning

A machine learning model using labeled datasets for training.

Label in Supervised Learning

Information used as a tag for data in supervised learning.

Classification Model

A supervised learning algorithm that categorizes data into discrete classes.

Regression Model

A supervised learning algorithm predicting continuous outcomes based on input data.

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Unsupervised Learning

A machine learning model that works with unlabeled data to find patterns.

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Patterns in Unsupervised Learning

Identified relationships or trends from unlabeled data by the machine.

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Continuous Data

Data that can take any value within a range, used in regression.

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Discrete Data

Data that consists of distinct or separate values, used in classification.

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AI Project Cycle Stages

The stages include Problem Scoping, Data Acquisition, Data Exploration, Modeling, and Evaluation.

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Sustainable Development Goals (SDGs)

Global Goals adopted to end poverty and protect the planet, ensuring peace and prosperity.

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4Ws of Problem Canvases

Who, What, Where, Why - key questions in problem scoping.

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Training Dataset

Data used to train an AI model to learn patterns.

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Testing Dataset

Data used to evaluate the performance of the trained AI model.

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Rule-Based AI Modeling

Approach where rules are defined by a developer; static and does not adapt.

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Learning-Based AI Modeling

Approach where the AI learns from data and adapts over time.

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Adaptive Learning

The ability of a model to adjust to new input data.

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Clustering

An unsupervised learning algorithm that groups unknown data by identifying patterns and trends.

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Dimensionality Reduction

A technique used to simplify complex data by reducing its number of dimensions for better visualization.

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Reinforcement Learning

A type of machine learning that enables agents to learn from experiences via rewards and punishments.

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Data Exploration

The stage of analyzing data to extract useful information and verify its quality.

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Interactive Learning

Learning that occurs in an environment where an agent interacts and receives feedback.

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Trial and Error

A method of learning involving repeated attempts and adjustments based on outcomes.

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Data Validation

The process of ensuring that collected data meets specified requirements and is error-free.

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Reliable Data Sources

Authentic places from which trustworthy and valid datasets can be collected.

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AI Project Training

Training an AI model requires data to predict outputs effectively.

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Data Acquisition

The process of gathering data from various sources for an AI project.

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Data Modelling

Selecting and testing models to achieve the goals established in the project.

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Model Evaluation

Testing the efficiency of the AI model using metrics like precision and recall.

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Problem Scoping

Defining the AI project's goals and understanding the parameters involved.

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4Ws Problem Canvas

A framework to define who is affected, what the problem is, where it occurs, and when it happens.

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Model Efficiency Parameters

Four key metrics used to evaluate an AI model: Acquisition, Precision, Recall, and F1 Score.

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Stakeholders in Problem Solving

Individuals affected by a problem and benefit from its solution.

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Nature of the Problem

Characteristics defining what the problem is and its evidence.

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Context of the Problem

The situation or location where the problem occurs.

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Benefits to Stakeholders

Advantages gained by stakeholders from an implemented solution.

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Features of Artificial Neural Networks

Characteristics of ANNs, mimicking human brain functions and extracting patterns automatically.

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Example of Rule-Based AI

Training a model with labeled images (like apples and bananas) to recognize them.

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Learning Based Approach

An AI model learns patterns from unlabeled data without developer-defined relationships.

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Artificial Neural Network (ANN)

A model that mimics the human brain, consisting of multiple layers of connected perceptrons.

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Input Layer

The layer in an ANN that receives all input data from the programmer.

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Hidden Layer

Layers between the input and output in an ANN where computations are performed.

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Output Layer

The final layer in an ANN that delivers the output after transformations.

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Problem Statement Template

A summary tool that captures key points of a problem for future reference.

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Evaluation in AI

The process of testing an AI model's reliability using a test dataset to compare outputs.

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Model Performance Testing

Evaluating the efficiency and effectiveness of an AI model against actual answers.

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AI Project Cycle PDF

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