Introduction to Machine Learning

What is the primary characteristic of supervised learning?

• Supervised learning only works with images
• Supervised learning requires labeled training data to make predictions or decisions.
• Supervised learning does not require any training data
• Unlabeled training data is used for making predictions

In supervised learning, what is the role of the model during training?

• Learn from the input data and adjust its parameters to minimize the difference between its predicted output and the actual output.

• Maximize the difference between its predicted output and the actual output

• Ignore the input data and focus on its initial parameters

• Generate random outputs without learning from the input data

Which of the following is an example of unsupervised learning?

• Linear regression
• Decision tree
• Clustering
• Logistic regression

What is the main goal of clustering algorithms in unsupervised learning?

• To predict future outcomes based on historical data
• Group similar data points together based on certain features or characteristics.

• To classify data into predefined categories
• To identify outliers in the dataset

Semi-supervised learning combines elements of both:

• Semi-supervised learning combines elements of both reinforcement learning and deep learning.
• Semi-supervised learning combines elements of both classification and clustering.
• Semi-supervised learning combines elements of both supervised and unsupervised learning.
• Semi-supervised learning combines elements of both regression and dimensionality reduction.

Which statement is true about semi-supervised learning?

• Semi-supervised learning only uses labeled data for training
• Semi-supervised learning uses a combination of labeled and unlabeled data for training.
• Semi-supervised learning does not require any data for training
• Semi-supervised learning is only suitable for image recognition tasks

What is the purpose of a validation set in supervised learning?

• Training the model

• Testing the model on unseen data

• Providing additional labeled data

• Tuning hyperparameters

Which algorithm is commonly used for classification in supervised learning?

• The commonly used algorithm for classification in supervised learning is PCA.

• The commonly used algorithm for classification in supervised learning is ANN.

• The commonly used algorithm for classification in supervised learning is the Support Vector Machine (SVM).
• The commonly used algorithm for classification in supervised learning is Linear Regression.

In unsupervised learning, which technique is used to reduce the dimensionality of the data?

• Decision Tree
• Linear Regression
• K-means Clustering
• Principal Component Analysis (PCA)

What is an advantage of using semi-supervised learning over purely supervised learning?

• Semi-supervised learning requires larger labeled datasets than purely supervised learning
• Semi-supervised learning has lower model performance compared to purely supervised learning
• Semi-supervised learning can make use of unlabeled data, which can improve model performance and reduce the need for large labeled datasets.
• Semi-supervised learning cannot make use of unlabeled data