CIS 4930 - AI Exam #1 Questions with Correct Answers Latest Update 2025-2026
• Define machine learning. - Answers A set of methods that can automatically detect patterns in
data, and then use the uncovered patterns to predict future data or other outcomes of interest.
Programming computers to optimize a performance criterion using examples data or past
experience.
What is the main difference between machine learning (ML) and traditional programming? -
Answers Machine learning involves training a model using data to recognize patterns and make
decisions, while traditional programming requires explicit instructions written by a programmer
for the computer to follow.
How does machine learning differ from traditional programming in terms of learning
methodology? - Answers In machine learning, the model learns the solution by recognizing
patterns in data without specific instructions, while in traditional programming, the programmer
writes explicit instructions for the computer to follow.
What are some advantages of using machine learning (ML) over traditional programming? -
Answers Adaptability: ML can adapt over time with more data.
Data Dependency: ML performance is significantly impacted by the quality and quantity of data.
Complexity and Scalability: ML can handle complex and high-dimensional problems effectively
and scale with more data.
• What are the key differences between supervised and unsupervised learning? - Answers In
supervised learning, the training data you feed to the algorithm includes the desired solutions,
called labels. A labeled training set for supervised learning (e.g., spam classification).
A typical supervised learning task is classification. The spam filter is a good example of this: it
is trained with many example emails along with their class (spam or ham), and it must learn
how to classify new emails. Another typical task is to predict a target numeric value, such as the
price of a car, given a set of features (mileage, age, brand, etc.) called predictors. This sort of
task is called regression.
To train the system, you need to give it many examples of cars, including both their predictors
,and their labels. Here are some of the most important supervised learning algorithms: k-Nearest
Neighbors. Linear Regression. Logistic Regression. Support Vector Machines (SVMs). Decision
Trees and Random Forests Neural networks.
In unsupervised learning, as you might guess, the training data is unlabeled. The system tries to
learn without a teacher.
An unlabeled training set for unsupervised learning. Here are some of the most important
unsupervised learning algorithms: Clustering k-Means. Hierarchical Cluster Analysis (HCA).
Expectation Maximization Visualization. Dimensionality reduction. Principal Component
Analysis (PCA). Kernel PCA. Locally-Linear Embedding (LLE). t-distributed Stochastic Neighbor
Embedding (t-SNE).
• Describe a real-world application of machine learning. - Answers Classification
• Disease diagnosis and prediction
• Spam Filtering
• Human activity recognition
Regression
• Estimating house prices based on location, size, age, amenities, and market conditions.
Recommendation Systems
• Predicting new searches on Amazon or eBay based on past searches.
Computer Vision
• Deep Learning (Automated Vehicles)
• Image recognition
Learning to Play Games
, • Chess, Board Games, NPC, AI.
Anomaly Detection
• Credit Card and Fraud Detection: Analyzing transaction patterns to identify and prevent
unauthorized credit card use.
What is the main difference between machine learning (ML) and Data Science? - Answers While
data science integrates statistics, and data analysis, machine learning understands and
analyzes the actual phenomena with data. ML focuses on creating and improving algorithms
that can learn from historical data to predict future outcomes or for classification purposes. On
the other hand, DS involves understanding and acting on data to drive decision making; it also
involves data visualization, manipulation, storage and interpretation.
ML apps are focused on tasks that involve prediction while DS apps optimize business
processes, A/B testing and data products in the hopes of making better strategic decisions
based on data. It's about extracting from data to inform business strategy.
ML aims to develop models that can make accurate predictions based on input data, while DS
aims to use input data to solve real-world problems that provide actionable insights.
• Define supervised learning. - Answers • Labeled Data
• Direct Feedback
• Predict Outcome/Future
• Two Tasks: Classification (1-of-N inputs) and Regression (real-valued output).
In supervised learning, the training data you feed to the algorithm includes the desired solutions,
called labels. A labeled training set for supervised learning.
A typical supervised learning task is classification. The spam filter is a good example of this: it
is trained with many example emails along with their class (spam or ham), and it must learn
how to classify new emails. Another typical task is to predict a target numeric value, such as the
price of a car, given a set of features (mileage, age, brand, etc.) called predictors. This sort of
task is called regression.
• Define machine learning. - Answers A set of methods that can automatically detect patterns in
data, and then use the uncovered patterns to predict future data or other outcomes of interest.
Programming computers to optimize a performance criterion using examples data or past
experience.
What is the main difference between machine learning (ML) and traditional programming? -
Answers Machine learning involves training a model using data to recognize patterns and make
decisions, while traditional programming requires explicit instructions written by a programmer
for the computer to follow.
How does machine learning differ from traditional programming in terms of learning
methodology? - Answers In machine learning, the model learns the solution by recognizing
patterns in data without specific instructions, while in traditional programming, the programmer
writes explicit instructions for the computer to follow.
What are some advantages of using machine learning (ML) over traditional programming? -
Answers Adaptability: ML can adapt over time with more data.
Data Dependency: ML performance is significantly impacted by the quality and quantity of data.
Complexity and Scalability: ML can handle complex and high-dimensional problems effectively
and scale with more data.
• What are the key differences between supervised and unsupervised learning? - Answers In
supervised learning, the training data you feed to the algorithm includes the desired solutions,
called labels. A labeled training set for supervised learning (e.g., spam classification).
A typical supervised learning task is classification. The spam filter is a good example of this: it
is trained with many example emails along with their class (spam or ham), and it must learn
how to classify new emails. Another typical task is to predict a target numeric value, such as the
price of a car, given a set of features (mileage, age, brand, etc.) called predictors. This sort of
task is called regression.
To train the system, you need to give it many examples of cars, including both their predictors
,and their labels. Here are some of the most important supervised learning algorithms: k-Nearest
Neighbors. Linear Regression. Logistic Regression. Support Vector Machines (SVMs). Decision
Trees and Random Forests Neural networks.
In unsupervised learning, as you might guess, the training data is unlabeled. The system tries to
learn without a teacher.
An unlabeled training set for unsupervised learning. Here are some of the most important
unsupervised learning algorithms: Clustering k-Means. Hierarchical Cluster Analysis (HCA).
Expectation Maximization Visualization. Dimensionality reduction. Principal Component
Analysis (PCA). Kernel PCA. Locally-Linear Embedding (LLE). t-distributed Stochastic Neighbor
Embedding (t-SNE).
• Describe a real-world application of machine learning. - Answers Classification
• Disease diagnosis and prediction
• Spam Filtering
• Human activity recognition
Regression
• Estimating house prices based on location, size, age, amenities, and market conditions.
Recommendation Systems
• Predicting new searches on Amazon or eBay based on past searches.
Computer Vision
• Deep Learning (Automated Vehicles)
• Image recognition
Learning to Play Games
, • Chess, Board Games, NPC, AI.
Anomaly Detection
• Credit Card and Fraud Detection: Analyzing transaction patterns to identify and prevent
unauthorized credit card use.
What is the main difference between machine learning (ML) and Data Science? - Answers While
data science integrates statistics, and data analysis, machine learning understands and
analyzes the actual phenomena with data. ML focuses on creating and improving algorithms
that can learn from historical data to predict future outcomes or for classification purposes. On
the other hand, DS involves understanding and acting on data to drive decision making; it also
involves data visualization, manipulation, storage and interpretation.
ML apps are focused on tasks that involve prediction while DS apps optimize business
processes, A/B testing and data products in the hopes of making better strategic decisions
based on data. It's about extracting from data to inform business strategy.
ML aims to develop models that can make accurate predictions based on input data, while DS
aims to use input data to solve real-world problems that provide actionable insights.
• Define supervised learning. - Answers • Labeled Data
• Direct Feedback
• Predict Outcome/Future
• Two Tasks: Classification (1-of-N inputs) and Regression (real-valued output).
In supervised learning, the training data you feed to the algorithm includes the desired solutions,
called labels. A labeled training set for supervised learning.
A typical supervised learning task is classification. The spam filter is a good example of this: it
is trained with many example emails along with their class (spam or ham), and it must learn
how to classify new emails. Another typical task is to predict a target numeric value, such as the
price of a car, given a set of features (mileage, age, brand, etc.) called predictors. This sort of
task is called regression.
