providing a list of the 80 most common “artificial intelligence” interview questions would be impractical and it would depend on the specific role and company. However, I can provide you with some general questions that could be asked in an AI interview:
- Can you explain the difference between supervised and unsupervised learning?
- What is the purpose of a neural network?
- What is the difference between deep learning and machine learning?
- Can you explain the concept of overfitting in a model?
- What is the difference between a generative and discriminative model?
- How do you handle missing data in a dataset?
- Can you explain the concept of gradient descent?
- How do you evaluate the performance of a machine learning model?
- Can you explain the concept of regularization in a model?
- How do you handle a class imbalance in a dataset?
- Can you explain the concept of entropy in information theory?
- How do you handle categorical variables in a dataset?
- Can you explain the concept of reinforcement learning?
- What is the difference between L1 and L2 regularization?
- Can you explain the concept of Bayesian statistics?
- How do you prevent a model from becoming biased?
- Can you explain the concept of a decision tree?
- How do you handle time series data in a dataset?
- Can you explain the concept of support vector machines?
- How do you select features for a machine learning model?
- Can you explain the concept of natural language processing (NLP)?
- How do you handle text data in a dataset?
- Can you explain the concept of a hidden Markov model (HMM)?
- How do you evaluate the performance of an NLP model?
- Can you explain the concept of sentiment analysis?
- How do you handle missing data in a time series dataset?
- Can you explain the concept of a recurrent neural network (RNN)?
- How do you handle outliers in a dataset?
- Can you explain the concept of a convolutional neural network (CNN)?
- How do you handle multicollinearity in a dataset?
- Can you explain the concept of a generative adversarial network (GAN)?
- How do you handle imbalanced classes in a dataset?
- Can you explain the concept of a transformer model?
- How do you select a model for a specific problem?
- Can you explain the concept of an autoencoder?
- How do you handle missing data in a categorical variable?
- Can you explain the concept of a long short-term memory (LSTM) network?
- How do you handle high-dimensional data?
- Can you explain the concept of a self-organizing map (SOM)?
- How do you handle non-stationary time series data?
- Can you explain the concept of a Hopfield network?
- How do you handle missing data in a continuous variable?
- Can you explain the concept of a Boltzmann machine?
- How do you handle categorical variables with multiple levels?
- Can you explain the concept of a Restricted Boltzmann machine (RBM)?
- How do you handle categorical variables with high cardinality?
- Can you explain the concept of a deep belief network (DBN)?
- How do you handle correlated variables in a dataset?
- Can you explain the concept of a stacked autoencoder?
- How do you handle high-dimensional categorical variables?
- Can you explain the concept of transfer learning?
- How do you handle imbalanced classes in a time series dataset?
- Can you explain the concept of a capsule network?
- How do you handle missing data in a text dataset?
- Can you explain the concept of a variational autoencoder (VAE)?
- How do you handle categorical variables with missing levels?
- Can you explain the concept of an attention mechanism?
- How do you handle categorical variables with rare levels?
- Can you explain the concept of a normalizing flow?
- How do you handle categorical variables with high dimensionality?
- Can you explain the concept of adversarial training?
- How do you handle categorical variables with multiple rare levels?
- Can you explain the concept of meta-learning?
- How do you handle categorical variables with multiple high-dimensional levels?
- Can you explain the concept of one-shot learning?
- How do you handle categorical variables with multiple rare high-dimensional levels?
- Can you explain the concept of zero-shot learning?
- How do you handle categorical variables with multiple missing levels?
- Can you explain the concept of lifelong learning?
- How do you handle categorical variables with multiple rare missing levels?
- Can you explain the concept of few-shot learning?
- How do you handle categorical variables with multiple high-dimensional missing levels?
- Can you explain the concept of meta-learning?
- How do you handle high-dimensional categorical variables with missing levels?
- Can you explain the concept of adversarial examples?
- How do you handle high-dimensional categorical variables with rare levels?
- Can you explain the concept of the interpretability of AI models?
- How do you handle high-dimensional categorical variables with multiple levels?
- Can you explain the concept of an explainable AI (XAI)?
- How do you handle high-dimensional categorical variables with missing levels?
