Interview questions on Artificial Intelligence (AI) and Machine Learning (ML) asked in multinational corporations (MNCs), along with explanations:
- What is the difference between supervised learning, unsupervised learning, and reinforcement learning? Can you provide examples of algorithms for each type?
- Supervised Learning: In supervised learning, the algorithm is trained on a labeled dataset, where each input is associated with a corresponding output. The goal is to learn a mapping from inputs to outputs.
- Example algorithms: Linear Regression, Decision Trees, Support Vector Machines, Neural Networks.
- Unsupervised Learning: Unsupervised learning involves training the algorithm on an unlabeled dataset, where the model learns patterns and structures from the input data without explicit guidance.
- Example algorithms: K-means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), Autoencoders.
- Reinforcement Learning: Reinforcement learning is a type of learning where an agent learns to interact with an environment by taking actions and receiving feedback in the form of rewards or penalties. The goal is to learn a policy that maximizes cumulative rewards.
- Example algorithms: Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods, Actor-Critic Methods.
- Supervised Learning: In supervised learning, the algorithm is trained on a labeled dataset, where each input is associated with a corresponding output. The goal is to learn a mapping from inputs to outputs.
- Explain the bias-variance tradeoff in machine learning. How do you mitigate overfitting and underfitting?
- Bias: Bias refers to the error introduced by approximating a real-world problem with a simplified model. High bias models tend to underfit the data.
- Variance: Variance refers to the model’s sensitivity to fluctuations in the training data. High variance models tend to overfit the data.
- The bias-variance tradeoff represents a balance between the complexity of the model and its ability to generalize to unseen data.
- To mitigate overfitting (high variance):
- Use simpler models or reduce model complexity (e.g., fewer features, shallower networks).
- Regularization techniques such as L1/L2 regularization, dropout, and early stopping.
- To mitigate underfitting (high bias):
- Use more complex models (e.g., adding more features, increasing model capacity).
- Collect more data to provide more information to the model.
- Can you explain the process of feature engineering in machine learning? Why is it important, and what techniques can you use?
- Feature Engineering: Feature engineering involves selecting, transforming, and creating input features from raw data to improve model performance and generalization.
- Importance: Feature engineering is crucial because the quality of input features directly impacts the model’s ability to learn and make accurate predictions.
- Techniques:
- Feature Selection: Choosing relevant features and eliminating irrelevant or redundant ones to reduce dimensionality and improve model interpretability.
- Feature Transformation: Scaling, normalization, and encoding categorical variables to ensure features are on a similar scale and suitable for modeling.
- Feature Creation: Generating new features by combining existing features, extracting meaningful information, or creating domain-specific features.
- Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE) to reduce the number of features while preserving important information.
- Evaluation: Validate feature engineering choices using cross-validation or hold-out validation to ensure improvements in model performance are not due to overfitting to a particular dataset.
Preparing for these questions with clear explanations and examples can help candidates demonstrate their knowledge and expertise in AI/ML during interviews at MNCs.