Artificial Intelligence
Interview Questions

In supervised learning, the algorithm is trained on a labeled dataset, where the input data is paired with corresponding output labels. Unsupervised learning involves training on unlabeled data, and the algorithm discovers patterns and relationships on its own.

Reinforcement learning involves training an agent to make decisions in an environment by providing feedback in the form of rewards or penalties. An example is training a computer program to play games, where it learns to maximize rewards by making strategic decisions.

Deep learning involves neural networks with multiple layers (deep neural networks). It excels in tasks like image and speech recognition. Applications include autonomous vehicles, natural language processing, and medical image analysis.

AI is the broader concept of creating machines that can perform tasks requiring human intelligence. Machine learning is a subset of AI, focusing on the development of algorithms that allow computers to learn from data.

NLP enables machines to understand, interpret, and generate human language. Applications include chatbots, language translation, sentiment analysis, and voice assistants like Siri or Alexa.

Ethical considerations in AI include bias, privacy, and job displacement. Addressing these involves implementing fairness in algorithms, ensuring data privacy, and actively participating in discussions around the responsible development and use of AI technologies.

It's essential to prioritize the interpretability of models, especially in sensitive applications. Techniques such as using simpler models, feature importance analysis, and model-agnostic interpretability methods help in understanding and explaining model decisions.

Transfer learning involves pre-training a model on a large dataset and fine-tuning it for a specific task with a smaller dataset. It allows leveraging knowledge gained from one task to improve performance on another, saving computational resources.

Overfitting occurs when a model performs well on training data but poorly on unseen data. Regularization techniques, cross-validation, and using more data are common approaches to mitigate overfitting.

The bias-variance tradeoff is the balance between model simplicity (high bias) and flexibility (high variance). Finding the right balance minimizes errors on both training and test datasets. High bias can result in underfitting, and high variance can lead to overfitting.

Challenges include model scalability, monitoring, and maintaining model performance over time. Addressing these requires collaboration with DevOps, implementing robust monitoring systems, and regular model re-evaluation.

Bagging (Bootstrap Aggregating) combines predictions from multiple models trained on random subsets of the dataset. Boosting, on the other hand, focuses on training weak learners sequentially, giving more weight to misclassified instances in each iteration.

Model performance is assessed using metrics such as accuracy, precision, recall, F1 score, and area under the ROC curve (AUC-ROC). It's important to choose metrics based on the specific goals and characteristics of the problem at hand.

Deep reinforcement learning combines deep learning with reinforcement learning. An example is training a computer program to play complex games, where the agent learns to make decisions through trial and error.

Techniques for handling imbalanced datasets include resampling methods (oversampling minority class or undersampling majority class), using different evaluation metrics, and exploring ensemble methods.

ANI refers to AI systems that excel in specific tasks, while AGI is a hypothetical system with the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence.

In a sentiment analysis project, I used LIME (Local Interpretable Model-agnostic Explanations) to provide human-interpretable explanations for model predictions. This helped in understanding which features influenced the sentiment predictions.

Handling missing data involves techniques such as imputation (mean, median, or mode), using algorithms that handle missing values inherently, or excluding rows or columns with missing data based on the extent of missingness.

Hyperparameter tuning involves optimizing the configuration settings of a model. Techniques include grid search, random search, and more advanced methods like Bayesian optimization. It's crucial to balance computational resources with the search space to find optimal hyperparameters.