ML UNIT 1 QUIZ PART 1

What is the main goal of machine learning?

• To program computers without human intervention

• To enable computers to learn from data and improve performance over time

• To create AI systems that can outperform humans

• To develop complex algorithms for data processing

Which type of machine learning algorithm is trained on labeled data to make predictions on new, unseen data?

• Unsupervised Learning

• Reinforcement Learning

• Semi-supervised Learning

• Supervised Learning

What is the process of preparing raw data by cleaning, transforming, and normalizing it for machine learning?

• Data Preprocessing

• Data Engineering

• Data Wrangling

• Data Augmentation

In unsupervised learning, the primary task is

• Predicting an output value based on input data

• Discovering patterns or structures in data

• Maximizing cumulative rewards through interactions with the environment

• Learning from expert demonstrations

Which machine learning algorithm is inspired by the behaviour of neurons in the human brain?

• Decision Trees

• k-Nearest Neighbours (k-NN)

• Support Vector Machines (SVM)

• Artificial Neural Networks (ANN)

What is the process of feeding a machine learning model with data to adjust its internal parameters and improve performance?

• Model Validation

• Model Optimization

• Model Training

• Model Testing

Which machine learning technique allows models to make decisions based on past experiences and feedback from their environment?

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

• Semi-supervised Learning

What is the primary advantage of using a deep learning architecture for machine learning tasks?

• Easy interpretability of the model

• Faster training time compared to traditional algorithms

• Ability to automatically extract hierarchical features from data

• Less need for large amounts of labeled training data

Which technique is used for reducing the dimensionality of data while preserving its most important features?

• Principal Component Analysis (PCA)

• Linear Regression

• Logistic Regression

• Gradient Descent

What does VC dimension do?

• Reduces complexity of hypothesis space

• Removes noise from dataset

• Measures complexity of training dataset

• Measures the complexity of hypothesis space H