1. Core Machine Learning Algorithms:
- Explain the differences between Bagging and Boosting. How do they improve the performance of weak learners?
- Can you describe the working of XGBoost and how it differs from other gradient boosting techniques?
- How does the Random Forest algorithm handle missing data, and what are the key parameters you would tune in Random Forest?
- Explain Support Vector Machines (SVM) and the significance of the kernel trick. When would you use a linear kernel vs. an RBF kernel?
- In Reinforcement Learning, explain the concepts of Q-Learning and Policy Gradient. How do they differ in their approach to learning?
2. Mathematical Foundations:
- What is the bias-variance tradeoff, and how does it impact model selection?
- Explain Principal Component Analysis (PCA). How do you select the number of components?
- How do you derive the gradient of the loss function in logistic regression?
- Explain Eigenvalues and Eigenvectors. How are they used in the context of machine learning?
- What is the Frobenius norm, and how is it used in matrix regularization?
3. Model Evaluation & Selection:
- What metrics would you use to evaluate a classification model on an imbalanced dataset? How do precision-recall and ROC-AUC curves differ in their evaluation?
- Explain the concept of cross-validation. How does k-fold cross-validation work, and when would you use stratified k-fold cross-validation?
- How do you handle overfitting in neural networks? Explain the role of dropout, early stopping, and L2 regularization.
- How do you deal with high false positive or false negative rates in a model? How would you modify your model to reduce them?
4. Optimization Techniques:
- Explain the difference between Stochastic Gradient Descent (SGD), Mini-batch Gradient Descent, and Batch Gradient Descent. Which one is more efficient and why?
- What are Adam and RMSProp optimizers? How do they differ from traditional gradient descent?
- Explain backpropagation in neural networks. How does the chain rule apply in backpropagation?
- What is Gradient Clipping, and when would you use it in training deep learning models?
- How does Hyperparameter Optimization work? Explain grid search vs. random search vs. Bayesian optimization.
5. Deep Learning Concepts:
- What are Convolutional Neural Networks (CNNs), and how do they differ from Fully Connected Neural Networks?
- Explain the role of LSTM and GRU in Recurrent Neural Networks (RNNs). When would you prefer one over the other?
- How do Attention Mechanisms work in Transformer models, and why are they more effective for sequence data than traditional RNNs?
- What are autoencoders and their applications in dimensionality reduction and anomaly detection?
- Explain Batch Normalization and its role in training deep neural networks. How does it improve training speed and model performance?
6. Model Interpretability:
- What is SHAP (SHapley Additive exPlanations), and how is it used to explain model predictions?
- Explain LIME (Local Interpretable Model-Agnostic Explanations) and how it differs from SHAP.
- How would you interpret a Random Forest model? How can feature importance be derived from tree-based models?
- What is Partial Dependence Plot (PDP), and how is it used to interpret machine learning models?
- What methods can you use to ensure that a model is not biased, particularly in sensitive areas like healthcare or finance?
7. Feature Engineering:
- How would you deal with high cardinality categorical features in your dataset?
- Explain the concept of feature interaction and how you can capture it automatically in machine learning models.
- What is embedding, and how is it useful in representing categorical data or natural language?
- How would you handle missing data in a dataset? What are some advanced imputation techniques?
- What is Feature Scaling, and why is it important? When would you use standardization vs. normalization?
8. Model Deployment & Production:
- How would you deploy a machine learning model in a production environment? What are the key challenges?
- Explain the concept of model drift and data drift. How do you monitor and handle these in production?
- How would you design an A/B testing experiment for a machine learning model in production?
- What are the considerations for deploying real-time inference vs. batch inference models?
- How do you handle model versioning and rollbacks in production?
9. Unsupervised Learning:
- Explain the K-means clustering algorithm. How do you determine the optimal number of clusters?
- What is Hierarchical Clustering, and when would you use it over K-means?
- How does DBSCAN (Density-Based Spatial Clustering of Applications with Noise) work, and what are its advantages over K-means?
- Explain Gaussian Mixture Models (GMM). How are they used for clustering?
- What is t-SNE and UMAP, and how do they help in visualizing high-dimensional data?
10. Recommender Systems:
- What is Collaborative Filtering, and how does it differ from Content-Based Filtering in recommender systems?
- How would you handle the cold start problem in recommender systems?
- Explain Matrix Factorization in the context of recommender systems. How does it work with large sparse matrices?
- How do Hybrid Recommender Systems work, and what are the advantages of combining collaborative and content-based methods?
- How do you evaluate the performance of a recommender system? What metrics would you track (e.g., precision@k, recall@k)?
11. Time Series Forecasting:
- How do you handle seasonality and trend in time series forecasting models?
- Explain ARIMA (AutoRegressive Integrated Moving Average) and how it is used in time series forecasting.
- What is Prophet by Facebook, and how does it handle time series forecasting?
- How would you incorporate exogenous variables in a time series forecasting model?
- What are some advanced techniques like LSTMs and GRUs for time series data, and when would you prefer these over traditional models like ARIMA?
12. Industry Applications & Real-World Scenarios:
- Can you describe a machine learning project you worked on, focusing on a real-world problem? What were the key challenges, and how did you solve them?
- How would you handle imbalanced datasets in domains such as fraud detection or medical diagnosis?
- Explain your approach to building an end-to-end machine learning pipeline in a production setting.
- In self-driving cars, how does machine learning interact with computer vision and sensor data to make decisions?
- How do you use machine learning in domains like natural language processing (NLP), computer vision, or speech recognition?
These questions assess a candidate’s ability to not only apply machine learning concepts but also deploy and manage models in real-world settings. Advanced candidates should be able to explain complex topics clearly and demonstrate practical knowledge through examples from their experience.
