Types of Machine Learning (Supervised, Unsupervised, Reinforcement) Questions and Answers for Viva

Answer-1: The three main types of Machine Learning are supervised learning, unsupervised learning, and reinforcement learning.

Answer-2: Supervised learning is a type of Machine Learning where the model is trained on labeled data. The algorithm learns to map input data to the correct output based on the provided labels.

Answer-3: Unsupervised learning involves training a model on unlabeled data. The model tries to find hidden patterns or structures in the data without any predefined labels.

Answer-4: Reinforcement learning is a type of Machine Learning where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties based on the actions it takes.

Answer-5: In supervised learning, the model is trained on labeled data, whereas in unsupervised learning, the model is trained on unlabeled data, and it must identify patterns or clusters.

Answer-6: Supervised learning is used for tasks like classification (e.g., email spam detection, medical diagnosis) and regression (e.g., stock price prediction, housing price forecasting).

Answer-7: Labeled data refers to data that has both input features and corresponding output labels, allowing the model to learn the relationship between them during training.

Answer-8: Common algorithms in supervised learning include linear regression, logistic regression, decision trees, support vector machines (SVM), and k-nearest neighbors (KNN).

Answer-9: The main goal of unsupervised learning is to find hidden patterns, structures, or relationships in the data without the use of labeled output data.

Answer-10: Common algorithms in unsupervised learning include k-means clustering, hierarchical clustering, and principal component analysis (PCA).

Answer-11: Clustering is a technique in unsupervised learning where the algorithm groups similar data points into clusters based on their features, such as in customer segmentation or image grouping.

Answer-12: Dimensionality reduction is the process of reducing the number of input variables or features in the data, making it easier to visualize or model while preserving the important information. Techniques like PCA and t-SNE are commonly used.

Answer-13: Reinforcement learning involves learning through interaction with an environment by receiving feedback in the form of rewards or penalties, whereas supervised and unsupervised learning involve learning from data.

Answer-14: The key components of a reinforcement learning system include the agent, environment, actions, states, and rewards. The agent takes actions to maximize cumulative reward from the environment.

Answer-15: The reward in reinforcement learning is a numerical value that the agent receives after performing an action in the environment. It provides feedback on the action's success or failure.

Answer-16: The exploration-exploitation tradeoff refers to the dilemma where an agent must decide whether to explore new actions to find better long-term rewards (exploration) or to exploit known actions that have already yielded high rewards (exploitation).

Answer-17: A Markov Decision Process (MDP) is a mathematical framework used to describe the environment in reinforcement learning, consisting of states, actions, rewards, and transitions between states.

Answer-18: Q-learning is a model-free reinforcement learning algorithm that helps an agent learn the optimal action-value function, guiding it to select the best action in each state to maximize cumulative reward.

Answer-19: Deep reinforcement learning combines reinforcement learning with deep learning techniques, using deep neural networks to approximate value functions and policies, allowing it to handle complex environments with large state spaces.

Answer-20: A real-world example of supervised learning is email spam filtering, where the algorithm is trained on labeled emails (spam or not) to classify incoming emails as spam or not.

Answer-21: A real-world example of unsupervised learning is customer segmentation in marketing, where clustering algorithms group customers into segments based on purchasing behavior or demographics.

Answer-22: A reward function defines the goal of the agent by providing feedback based on its actions. It determines how well the agent is performing and guides it toward maximizing cumulative reward.

Answer-23: An episode in reinforcement learning is a complete sequence of interactions between the agent and the environment, starting from an initial state and ending when a terminal state is reached.

Answer-24: A policy in reinforcement learning is a strategy used by the agent to decide which action to take in each state based on its experiences and current knowledge.

Answer-25: In classification, the output is categorical, like "yes" or "no" or "spam" and "not spam," whereas in regression, the output is continuous, such as predicting house prices or stock prices.

Answer-26: The goal of unsupervised learning is to identify underlying structures, patterns, or groupings in the data, such as clustering similar data points or reducing data dimensions.

Answer-27: Feature selection in supervised learning involves identifying the most relevant features that contribute to the model's prediction and removing irrelevant or redundant ones to improve model performance.

Answer-28: A model-based approach uses a model of the environment to predict outcomes of actions, while a model-free approach directly learns from experiences, without a predefined model of the environment.

Answer-29: Unsupervised learning is useful in real-world problems like anomaly detection, clustering, and feature extraction, where labeled data is unavailable or difficult to obtain.

Answer-30: Reinforcement learning agents learn over time by interacting with the environment, observing the results of their actions (rewards or penalties), and refining their actions to maximize cumulative rewards.

Answer-31: In active reinforcement learning, the agent actively selects actions to maximize future rewards, while in passive reinforcement learning, the agent follows a fixed policy and evaluates it.

Answer-32: Reinforcement learning is commonly used in game AI to teach agents to make optimal decisions by playing the game repeatedly and learning from trial and error based on the rewards of their actions.

Answer-33: Reinforcement learning algorithms handle delayed rewards through techniques like temporal difference learning and reward discounting, where future rewards are given less weight than immediate rewards.

Answer-34: Unsupervised learning can be used for anomaly detection by identifying data points that deviate significantly from the majority of the data, indicating potential outliers or anomalies.

Answer-35: Supervised learning is used in financial fraud detection by training models on labeled data of fraudulent and legitimate transactions to classify new transactions as either fraudulent or not.

Answer-36: A neural network in reinforcement learning is used to approximate value functions or policies, especially in deep reinforcement learning, to handle complex, high-dimensional state spaces.

Answer-37: In off-policy learning, the agent learns from experiences generated by a different policy (e.g., Q-learning), while in on-policy learning, the agent learns from experiences generated by its own policy (e.g., SARSA).

Answer-38: The training set in supervised learning is used to train the model by providing labeled examples of input-output pairs, allowing the model to learn the relationship between inputs and outputs.

Answer-39: Generative models learn the joint probability distribution of input and output (e.g., Naive Bayes), while discriminative models learn the conditional probability distribution (e.g., logistic regression).

Answer-40: Exploration in reinforcement learning involves trying new actions that might lead to higher rewards in the future, helping the agent discover better strategies and improve performance.

Answer-41: Gradient descent is an optimization technique used in supervised learning to minimize the loss function by adjusting model parameters in the direction of the steepest decrease in the loss.

Answer-42: A decision tree is a supervised learning algorithm used for both classification and regression tasks. It splits the data into branches based on feature values to make predictions.

Answer-43: KNN is a non-parametric algorithm that classifies data based on the majority class of the nearest neighbors, while Naive Bayes is a probabilistic classifier that applies Bayes' theorem with strong independence assumptions.

Answer-44: In robotics, reinforcement learning is used to train robots to perform tasks like navigation, manipulation, and decision-making by learning from interactions with the environment.

Answer-45: Unsupervised learning can automatically identify important features in data by grouping similar data points together or reducing dimensionality, which helps improve the efficiency of downstream supervised models.

Answer-46: Feedback in reinforcement learning is provided through rewards or penalties based on the agent's actions, guiding the agent to learn optimal actions over time.

Answer-47: SVM is a supervised learning algorithm that finds the hyperplane that best separates different classes in the feature space, maximizing the margin between the classes.

Answer-48: Clustering groups similar data points together without any labels, while classification assigns labels to data points based on predefined categories in supervised learning.

Answer-49: Reinforcement learning can be used in autonomous vehicles to optimize driving strategies, such as lane changing and speed control, by rewarding safe and efficient driving behaviors.

Answer-50: The reward function in Q-learning provides feedback to the agent based on the actions it takes, allowing the agent to update its action-value estimates and learn optimal behaviors over time.