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• How can one reduce the effects of Swamping and Masking when using Isolation Forest for anomaly detection?
• Could you describe the core concept of the Isolation Forest algorithm, highlight its benefits for outlier detection, and explain the process of applying it to identify anomalies in data?
• How would you distinguish between out-of-distribution detection and anomaly detection?
• How can one-class SVM be applied for uncovering anomalies within a dataset?
• How do clustering methods operate when tasked with anomaly detection, and what are their principal traits for identifying unusual data points?
• How do clustering approaches identify outliers when clusters differ considerably in size?
• How would you use k-Means to identify outliers, and in which scenarios does k-Means produce suboptimal clusterings?
• If a dataset contains known outliers, would logistic regression still be a suitable modeling choice?
• How do Support Vector Machines and Logistic Regression differ in their approach to outliers?
• How can Standard Deviation be applied to detect anomalies in a dataset?
• How do Anomaly Detection and Behavior Detection differ in practical applications?
• Can you describe the three main classifications of anomaly detection methods?
• What are the major drawbacks encountered by density-based anomaly detection methods?
• What does it mean to perform anomaly detection, how is it applied in various real-world scenarios, why is finding anomalies important, and what sorts of anomalies exist in data?
• How can one measure how effective an anomaly detection model is?
• How do we define and utilize “explicit models” in anomaly detection, and what practical considerations are involved?
• How do outlier detection and novelty detection differ, and how are they each typically applied in real-world scenarios?
• How would you develop a system capable of detecting anomalies in streaming data in real time?
• How can Principal Component Analysis be leveraged to identify anomalies in a dataset?
• How is Dictionary Learning utilized to detect anomalies in data?
• What techniques do you use to handle outliers in a dataset?
• How can Mahalanobis distance serve as an approach for detecting anomalies in a dataset?
• Is it feasible to apply Support Vector Machines for identifying outliers?
• In the setting of evolving processes over time, how do we typically carry out anomaly detection?
• How can autoencoders be leveraged for detecting unusual or outlying patterns in data?
• How is the 68-95-99.7 principle relevant when describing a Normal Distribution?
• In a single-dimensional setting, how do anomalies differ when comparing uniform and normal distributions?
• In anomaly detection, how would you describe the issues known as Swamping and Masking, and why do they occur?
• How does a resolution-based outlier detection method work to find anomalies in data, and what is its underlying approach?
• In an anomaly detection context, how would you describe the concept of change detection and its significance?
• How is the interquartile range (IQR) applied in the setting of time series forecasting?
• How can Independent Ensemble Methods be utilized to detect anomalies in data?
• How do normalisation and standardisation differ in data preprocessing?
• Why is the median often chosen as the representative value of a dataset, rather than other central measures?
• How does the distance-based method work for identifying outliers, and what is the key principle behind it?
• How could clustering methods be used to detect unusual data points in a dataset?

• How does the adversarial approach help in eliminating biases during model training?
• How can we set up and manage the initialization of model parameters, such as weights and biases, when working with PyTorch?
• How do Bagging and Boosting methods differ in the field of ensemble learning?
• Could you explain the nature of the bias-variance tradeoff in machine learning and suggest approaches to address excessive bias?
• If a model consistently underfits the training data, how can you spot this issue and what measures can you take to resolve it?
• How would you explain bias error in predictive modeling, and how does it compare with variance error?
• How does the bias-variance tradeoff manifest itself within K-Nearest Neighbors?
• How can we recognize when a model exhibits high variance, and what techniques can we use to correct it?
• In the process of drawing a sample from a population, which types of sampling biases could potentially be introduced and how might they affect the final outcomes?
• Which forms of data bias commonly appear in machine learning pipelines, and what are their possible consequences?
• How can we provide a conceptual understanding of the balance between bias and variance when building predictive models?
• How do Content-Based approaches differ from Collaborative Filtering methods regarding bias and variance?

• What does it imply when an algorithm’s running time is characterized as o(log n)?
• How would you describe the concept of constant amortized time in the context of analyzing an algorithm’s time complexity?
• How would you describe what space complexity means, and can you illustrate it with pertinent examples?
• Explain how a lower bound differs from a tight bound in computational complexity.
• What does it indicate if we say an algorithm is characterized by O(n!) time complexity?
• Which popular algorithms from everyday usage have constant-time, logarithmic-time, and linearithmic-time complexities?

• How can Logistic Regression be applied as a supervised learning algorithm for classification?
• How does a multi-class classification approach differ from a multi-label classification approach?
• Could you compare the pros and cons of different classification algorithms, and how would you select the most suitable one in practice?
• What kinds of drawbacks or pitfalls could arise when using Naive Bayes for classification tasks?
• Is it possible to reformulate a regression task as classification, and can a classification task be turned into a regression problem?
• How do Bagging methods differ from Boosting methods in ensemble learning?
• How do Weak Learners differ from Strong Learners, and why can both be valuable?
• How do One-vs-Rest and One-vs-One strategies in multi-class classification differ from each other?
• Is it sensible to pick a classifier primarily by considering the size of your training data, and how should one approach this decision?
• Is it appropriate to apply Logistic Regression when the classes in a classification task are highly imbalanced?
• How do ROC curves differ from Precision–Recall curves?
• What factors motivate the use of Probability Calibration in machine learning models?
• How does the Naive Bayes algorithm function? Also, what is the rationale behind calling it “Naive”?
• How do Generative Classifiers differ from Discriminative Classifiers, and what are some common examples of both?
• Discuss various classification evaluation metrics and explain the contexts in which each one is most applicable.
• How does the Softmax function differ from the Sigmoid function?
• Could you describe the essence and purpose of a confusion matrix used in classification tasks?
• How do the ROC curve and AUC metric indicate a model’s quality, and in what way is the AUC-ROC curve employed for classification tasks?
• What are some benefits and downsides of relying on the AUC metric for model performance evaluation?
• Could you discuss the meaning of the F-score, and how one should interpret its numerical outcomes?
• How can you apply Naive Bayes to categorical attributes, and how would you adapt if some of the attributes are continuous?
• How would you contrast Naive Bayes against Logistic Regression when handling classification tasks?
• Could you explain in detail how K-Nearest Neighbors differs from the K-means Clustering method?
• How would you generate a classification prediction using a Logistic Regression model?
• What is the primary reason for applying the kernel trick in machine learning algorithms?
• What strategies would you use to adjust a classifier’s output probabilities so that they align more precisely with the actual likelihood of the classes?
• In a classification setting where one class heavily outweighs the other, how would you select the most suitable evaluation measure?
• How would you approach classification tasks when the data cannot be separated by a single linear boundary?
• How can a Confusion Matrix be leveraged to evaluate the quality of a classification model’s predictions?
• How are Random Oversampling and Random Undersampling different, and in which scenarios are they typically used?
• How would you describe the Akaike Information Criterion (AIC) in machine learning, and how is it typically applied for choosing models?

• What are the primary benefits and drawbacks of using k-Nearest Neighbors in machine learning?
• How do Decision Trees differ from k-Nearest Neighbors, and in what ways can they be compared with respect to performance and interpretability?
• Does the K-Nearest Neighbors method experience difficulties due to the Curse of Dimensionality, and if so, what leads to those challenges?
• When you apply k-Nearest Neighbors, which approach for normalizing your features is generally advised?
• How does the K-Nearest Neighbors approach connect to the tradeoff between bias and variance?
• How are K-Nearest Neighbors and Support Vector Machines different in their fundamental methods, and when might one approach surpass the other?
• How do k-Means and k-Nearest Neighbors fundamentally differ in their approach and purpose?
• Would the K-Nearest Neighbors algorithm be recommended when working with very large datasets, or are there specific concerns that might arise?
• How do k-Nearest Neighbors and Radius Nearest Neighbors differ from one another?

• Describe the approach used in Denoising Autoencoders
• How can Neural Networks be used to create Autoencoders?
• What are some differences between the Undercomplete Autoencoder and the Sparse Autoencoder?
• Can you use Batch Normalisation in Sparse Auto-encoders?
• How can you evaluate the Performance of an Autoencoder?
• Can autoencoders be used for feature generation? If yes, how?
• What are Variational Autoencoders?

• Under what conditions would you favor Hierarchical Clustering over k-Means?
• How could clustering be approached by applying evolutionary algorithms?
• How can you carry out a time-series clustering approach that focuses on individual observations?
• How would you describe a Latent Class Model in the context of machine learning?
• Why is it necessary to apply significance testing when clustering data?
• How would you describe Mean-Shift clustering, list its strengths and weaknesses, explain similarity-based clustering along with the Jaccard Index, and clarify what Self-Organizing Maps are?
• Why might Euclidean distance be a poor choice for data that is largely sparse?
• How do you determine the appropriate number of clusters when using a K-Medoids clustering approach?
• Why is K-Means often characterized by higher bias than Gaussian Mixture Models?
• How can we determine whether our dataset truly contains meaningful groupings before applying clustering algorithms?
• In which situations might it be preferable to apply segmentation instead of clustering?
• Explain the way DBSCAN organizes data points into clusters
• Why can Euclidean distance become problematic in high-dimensional data when applying clustering methods?
• How would you describe and explain a Gaussian Mixture Model that utilizes a Dirichlet Process?
• How would you describe the meaning of “mixture” when working with a Gaussian Mixture Model?
• What are the major distinctions between Hierarchical Clustering and k-Means Clustering?
• In what scenarios would it be more advantageous to apply Hierarchical Clustering instead of Spectral Clustering?
• What kinds of cluster structures do different clustering algorithms usually aim to identify?
• What sets apart the two primary variations of Hierarchical Clustering approaches?
• How would you decide on the most suitable distance metric among the many possible choices when performing clustering?
• How do Manhattan distance and Euclidean distance differ when applied to clustering tasks?

• How can cost functions be adapted in reinforcement learning policy-gradient methods when rewards are sparse or delayed, and why might adding “shaped” or auxiliary losses help?
• Why don't we use Mean Squared Error as a cost function in Logistic Regression?
• In unsupervised representation learning (e.g., autoencoders), how do you ensure the reconstruction-based cost function does not trivially learn to copy the input, and what techniques help mitigate this?
• Why might directly optimizing a non-differentiable metric like ROC AUC lead to practical challenges, and what are alternative approaches to incorporate it in the cost function?
• When applying label ranking losses in multi-label problems, how do you ensure the cost function properly accounts for label co-occurrences without assuming they are fully independent?
• In meta-learning, the “outer loop” optimizes a different objective than the “inner loop;” how do you design or reconcile these two cost functions to ensure the model generalizes across tasks?
• How can the concept of noise contrastive estimation (NCE) be viewed as defining an alternative cost function, and why is it beneficial in large-scale embedding or language modeling tasks?
• How would you fix Logistic Regression Overfitting problem?
• When optimizing a probabilistic model with continuous outputs, what are some advanced cost functions beyond simple Gaussian-based losses, and in which scenarios would you use them?
• In a survival analysis model, how would you adapt or design a cost function to handle censored data while still providing meaningful risk predictions?
• How does the design of a pairwise ranking cost function (e.g., for information retrieval) differ from standard classification losses, and why is negative sampling crucial?
• Why might Mean Absolute Error (MAE) be harder to optimize than Mean Squared Error (MSE) in high-dimensional feature spaces, and how could you mitigate those difficulties?
• What issues arise when the cost function must handle partially observed labels (e.g., partial annotation or weak supervision), and how could you address them?
• In a semi-supervised learning scenario, how do you balance unsupervised and supervised cost terms during training to ensure both signal sources are utilized effectively?
• What strategies exist to handle a cost function that yields vanishing or exploding gradients for very deep networks?
• How can label smoothing in classification tasks alter the landscape of the cross-entropy loss, and why might it improve generalization?
• Why might combining multiple cost components (e.g., reconstruction loss + classification loss) in a multi-task setup lead to optimization conflicts, and how can you resolve them?
• How would you incorporate task-specific constraints or domain knowledge directly into a custom cost function without relying solely on post-hoc regularization?
• What is the difference between Cost Function vs Gradient Descent?
• How could a mismatch between the training-time cost function and the inference-time usage lead to suboptimal real-world performance, and how would you address it?
• For Bayesian optimization of hyperparameters, the “cost function” is typically an expensive black-box evaluation; what surrogate or acquisition strategies can you use to guide the search efficiently?
• When training a model with a curriculum-learning approach, how can the dynamic definition of the cost function help the model learn progressively more complex examples?
• How can you handle sample-dependent costs (e.g., different misclassification penalties for different examples) in a standard deep learning framework?
• Why might you use an energy-based model’s cost function (e.g., contrastive divergence) in certain generative tasks, and how does it compare to typical likelihood-based losses?
• Can you explain how you might modify a regression loss function to explicitly penalize large negative predictions more than large positive ones in a revenue forecasting scenario?
• In latent variable models (e.g., VAEs), how do you interpret the role of the KL divergence term in the cost function, and how can a poorly tuned \beta term lead to posterior collapse?
• How do negative sampling approaches in word2vec or related embedding methods serve as a form of cost function design, and why might full softmax be impractical?
• What issues arise if you accidentally scale your loss by a factor that is too large or too small, and how can you systematically choose a proper loss scale?
• How would you design a cost function for a neural network that must produce a valid probability simplex across multiple outputs while also respecting a monotonicity constraint among those outputs?
• What is the Hinge Loss in SVM?
• In a knowledge distillation setup, how does the “teacher–student” training objective alter the standard cost function, and why might a temperature parameter be introduced?
• What complications can arise when using a non-convex cost function in a Bayesian setting, and how do approximate inference methods attempt to overcome those?
• In the presence of heavily skewed data distributions, why might re-scaling the cost function’s gradient updates (e.g., by class frequency) be more effective than just class-weighting in cross-entropy?
• If your cost function involves a Monte Carlo approximation (e.g., in variational inference or policy gradients), how do you reduce variance in the gradient estimates?
• When dealing with large models like GPT-style Transformers, the training cost function is typically a form of cross-entropy over the next token. How does the sequential nature of the problem affect the gradient flow and the shape of the cost landscape?
• Explain how you might design a custom loss function for a problem with unusual constraints (e.g., ordinal classification with specific label relationships). What pitfalls should you watch out for?
• In large-scale distributed training, the cost function is effectively split across multiple workers. How can this lead to potential issues (e.g., stale gradients), and how do frameworks address it?
• What strategy would you use to handle the situation where multiple objectives (e.g., accuracy, fairness, interpretability) need to be optimized simultaneously? How would you incorporate this into a single or multi-term cost function?
• Discuss how you would debug a situation where your training loss decreases steadily, but your validation metric (e.g., accuracy, F1-score) does not improve. How does this relate to the choice or definition of the cost function?
• How do you evaluate whether a chosen cost function is actually aligning with model performance in real-world metrics (like AUC, F1-score, or business KPIs)?
• In Reinforcement Learning, we often talk about a reward function rather than a cost function. How would you translate a reward function into a cost function, and what are potential pitfalls?
• When dealing with imbalanced datasets, what modifications or weighting strategies can be applied to the cost function to handle class imbalance? Give a concrete example.
• For deep metric learning (e.g., Siamese networks, triplet loss), how does the cost function differ from standard classification losses, and why is it crucial to choose an appropriate sampling strategy (e.g., hard negative mining)?
• Can you describe a scenario in which a theoretically correct cost function might not reflect the practical business objective, and how you would reconcile the two in a real-world system?
• What is the difference between Objective function, Cost function and Loss function
• Explain the concept of a loss function’s ‘gradient Lipschitz constant.’ Why is this property important for convergence guarantees in gradient-based methods?
• If your cost function is non-differentiable at certain points (e.g., absolute error or hinge loss), how do gradient-based optimization methods like (sub-)gradient descent still work?
• In the context of time-series forecasting, what unique considerations go into choosing or designing a cost function (beyond standard regression losses)?
• What do you understand by a ‘robust’ cost function? Give an example of a robust cost function and discuss scenarios where it is more appropriate than Mean Squared Error.
• Why might it be necessary to scale or normalize your features before defining a particular cost function, and how does this relate to the geometry of the cost function landscape?
• Describe how the shape of the cost function surface (convex vs. non-convex) can affect the optimization process. Give an example of a model or setting where non-convexity might be beneficial.
• Explain why the negative log-likelihood (NLL) loss is often used with certain output layers (e.g., softmax). When would you consider a different loss function for probabilistic outputs?
• In a multi-label classification problem, why might we prefer using a set of Binary Cross-Entropy losses (one per label) over a single Multiclass Cross-Entropy loss? Under what circumstance might this approach fail?
• How can the design of a cost function help avoid overfitting in deep neural networks without explicitly modifying the model architecture?
• Explain the role of regularization terms in the cost function. How does adding L_1 vs. L_2 regularization affect the shape and optimization landscape of the cost function?
• Explain what is Cost (Loss) Function in Machine Learning?
• What are some necessary Mathematical Properties a Loss Function needs to have in Gradient-Based Optimization?
• What Distance Function do you use for Quantitative Data?
• How would you choose the Loss Function for a Deep Learning model?
• Provide an analogy for a Cost Function in real life
• What type of Cost Functions do Greedy Splitting use?

• How could I (statistically) find features that are more important than others?
• How would you address the problem of Heteroskedasticity caused by a Measurement error?
• When would you use Sequential Split of data?
• How would you detect Heteroskedasticity?
• How do you find the Covariance Matrix from Missing Data?
• How can you create a model with a very Unbalanced dataset?
• When you sample, what potential Sampling Biases could you be inflicting?
• How does an ANOVA test work?
• What's the difference between One-vs-Rest and One-vs-One?
• How to perform Feature Engineering on Unknown features?
• Are there any troubles when using Early Stopping?
• When would you use Fine-Tuning vs Feature Extraction in Transfer Learning?
• How would you normalise Longitude/Latitude feature?
• What are some benefits of Scaling the Data for Neural Networks?
• Compare Causation vs Correlation
• What is Data Binning? When would you use Equal Frequency Binning and when do you use Equal Width Binning?
• How would you handle Missing Data and perform Data Imputation? How do you check if the Missing Data is Missing at Random (MAR) or Not?
• When would you use chi-Square or an ANOVA test?
• Is mean imputation of missing data acceptable practice? Why or why not?
• How you can extract useful features from Date and Time column if any?
• What is Multidimensional Scaling?
• Can Data Cleaning worsen the results of Statistical Analysis?
• What are some approaches to get a quantitative estimate of a model's Maximum Predictive Power given a certain level of noise?
• When you want (and do not want) to scale or standardize a variable prior to model fitting?
• Explain what is an Unrepresentative Dataset and how would you diagnose it?
• How does the Algorithm "The 10% You Don't Need" remove the Redundant Data?
• How does Normalization reduce the Dimensionality of the Data if you project the data to a Unit Sphere?
• How would you use a Confusion Matrix for determining a model performance?
• What's the difference between Random Oversampling and Random Undersampling and when they can be used?
• How would you deal with an Imbalanced Dataset?
• Are there any problems with splitting data randomly into Training, Validation, and Test datasets?
• What are the assumptions before applying the OLS estimator?
• Explain the two types of Data Reducing Algorithms
• How do you cope with Missing data in Regression?
• Does Redundant data affect an SVM-based classifier?
• Explain some Encoding techniques for Categorical Data
• What's the difference between stratifiedKFold (with shuffle = True) and StratifiedShuffleSplit in Scikit-Learn?
• When to use OneHotEncoder VS LabelEncoder in Scikit-Learn?
• What are some common practices for Preprocessing data for LLMs?
• What is the difference between Test Set and Validation Set?
• What is the l₁ Normalization of a data?
• Can you use different Normalization methods on different features?
• What are some common steps in Data Cleaning?
• If you are using k-Nearest Neighbors, what type of Normalization should be used?
• How do you Normalize Real-Time Data?
• How do you choose the Scaling method used for Neural Networks?
• How do you encode Categorical Data which consists of both Ordinal and Nominal data types?
• What are some recommended choices for Imputation Values?
• What is the difference between Non-dependency and Dependency-oriented data?
• Name some methods you know for Rebalancing a dataset using Rebalancing Design Pattern
• Why is data more sparse in a high-dimensional space?
• What's the difference between Covariance and Correlation?
• What is the difference between Normalization and Scaling?
• How can less Training Data give Higher Accuracy?

• When would you remove correlated variables?

• In which ways can Genetic Algorithms be used to refine a Deep Learning classifier’s architecture?
• You need to train a classifier and have access to abundant unlabeled data alongside only a few thousand labeled…
• How do Support Vector Machines differ from Deep Learning methods?
• Why is the ReLU activation function frequently chosen instead of Sigmoid in deep neural network architectures?
• How can the Fourier Transform be utilized to enhance Deep Learning performance and insights?
• What signs indicate that your model could be experiencing exploding gradients?
• What is the Gini Index, and in what manner is it applied within Decision Tree algorithms?
• How do Classification And Regression Trees generate decision boundaries for classification, and how do they generate…
• How do pre-pruning methods differ from post-pruning approaches when training a decision tree?
• How do out-of-bag (OOB) scores differ from validation scores in the context of Decision Trees, and what makes each…
• In what ways can you optimize a Random Forest model to achieve stronger predictive performance?
• What is the significance of pruning a decision tree, and why is it generally necessary?
• What factors determine when a decision tree should halt its growth?
• Explain how the “Greedy Splitting” method is applied when building a Decision Tree
• How can you handle a Decision Tree that appears to overfit its training data?
• How do we typically quantify the concept of information when constructing a decision tree?
• How do Gini Impurity and Entropy differ when constructing Decision Trees?
• How would you describe the procedure of using binary recursive splitting when creating a decision tree?
• How do Decision Trees define the variance reduction metric, and in what way is it employed during splitting?
• How do Decision Trees differ from Neural Networks in practical machine learning scenarios?
• Discuss how Decision Trees contrast with Logistic Regression, emphasizing strengths, weaknesses, and typical usage…
• How do we employ Isolation Forest for discovering anomalies in a dataset?
• How would you describe how Information Gain compares and relates to the Information Gain Ratio?
• How do you differentiate and evaluate the various methods for generating Decision Trees?
• Under what circumstances might one prefer Gini Impurity over Entropy for constructing a decision tree?
• How is Entropy employed when building Decision Trees?
• When constructing a decision tree, in what way do you determine which feature is used to split at every node?
• In what ways can a Decision Tree model be adapted for use in Collaborative Filtering tasks?
• How would you perform gradient boosting using decision trees?
• How does the concept of a Random Forest build upon the foundations of Decision Trees?
• What's the difference between Feature Engineering vs. Feature Selection? Name some benefits of Feature Selection
• What is the Importance of Data Reduction?
• What is the difference between Feature Selection and Feature Extraction?
• Would you use K-NN for large datasets?
• Should your Test Data be Cleaned the same way that the Training Data is?
• Why do ensemble methods typically achieve better performance when their constituent models have low correlation?
• How do Ensemble Learning and Multiple Kernel Learning fundamentally differ in their underlying principles and…
• Why does stacking multiple models often lead to better predictive performance?
• Is it feasible to utilize ensemble learning approaches for a quantile regression task?
• What does the concept of Ensemble Nyström methods refer to, and how do they differ from the standard Nyström approach…
• What are some key limitations of Decision Trees, and how can they be effectively addressed?
• How would you choose between using Random Forest and Gradient Boosted Trees for a specific problem?
• What are some situations in which Random Forests can be preferred over Neural Networks, and why might they be chosen?
• How do ensemble-based methods handle the challenge posed by the No-Free-Lunch principle?
• Is the LASSO approach a feasible way to select base learners in an ensemble model?
• How can you determine the best number of randomly selected features to consider at each decision split in a Random…
• How do high-dimensional feature spaces affect distance-based mining methods, and why does this occur?
• How do we quantify the computational cost involved in Ensemble Learning?
• How is Stacking employed as an ensemble approach, and what is its general mechanism in machine learning?
• In which situations is Grid Search preferred over Random Search, and vice versa, for tuning hyperparameters?
• How would you describe your understanding of dimensionality reduction?
• In PCA, what procedure is used to identify the first principal component axis?
• Explain what One-Hot Encoding and Label Encoding are, and discuss how each transformation impacts the dimensionality…
• How does the phenomenon called the "curse of dimensionality" influence privacy-preserving techniques?
• How would you explain the concept and workings of a Sparse Random Projection method?
• How do unsupervised clustering methods differ from dimensionality reduction, and how can they be connected in…
• What does the term "Crowding Problem" refer to?
• What are the main distinctions between Principal Component Analysis (PCA) and t-SNE for dimensionality reduction?
• How do Principal Component Analysis and Random Projection differ from each other?
• How does Random Projection help in lowering the dimensionality of a set of data points?
• What are the two principal categories of techniques for reducing dimensionality, and in what ways are they distinct?
• How can Isomap be used for Dimensionality Reduction, and what are the essential steps in its process?
• In reducing dimensionality, what factors make Locally Linear Embedding preferable to PCA?
• Why does a grid-based hyperparameter search method become highly susceptible to the Curse of Dimensionality?
• Could you explain how Locally Linear Embedding works for reducing the dimensionality of data?
• Do linear SVMs encounter difficulties due to the curse of dimensionality?
• In what ways do deep neural networks succeed in mitigating the curse of dimensionality?
• Does a Random Forest get significantly impacted by high-dimensional feature spaces, and does it suffer from the curse…
• In what ways does the curse of dimensionality influence the effectiveness of k-means clustering?
• What does t-Distributed Stochastic Neighbor Embedding do, and how does it work in reducing dimensionality?
• What guidelines are followed when creating the random matrix for Gaussian Random Projection?
• How would you explain the notion of Sparse PCA within the field of Machine Learning?
• Under what circumstances would you prefer to use manifold learning approaches instead of PCA?
• Why does data tend to become more spread out when the dimensionality of the feature space increases?
• How can one determine if a neural network is experiencing vanishing gradients?
• What is the primary purpose of adding an Embedding Layer to a neural network, and what key benefits does it offer?
• How would you go about selecting the activation function for a deep neural network?
• Why does a deep learning model generally become more accurate when given larger volumes of training data?
• How does randomly dropping certain network connections influence a Deep Learning model’s behavior and performance?
• When designing a deep neural network, what are the key considerations to keep in mind while picking a suitable loss…
• Is it possible to utilize autoencoders for creating features? If so, how can this be achieved?
• What modifications are required to transform a network’s Dense layer into a fully convolutional one?
• How does a standard autoencoder contrast with a variational autoencoder, and in which scenarios would one opt to use…
• How do Generative Adversarial Networks differ from autoencoders, and what sets them apart in how they learn latent…
• How would you define multi-task learning and in which circumstances is it best utilized?
• How can you illustrate two different techniques to visualize the internal representations learned by a convolutional…
• How do Batch Normalization,Instance Normalization & Layer Normalization differ & can you describe any challenges that…
• Explain what Generative Adversarial Networks are, point out the most common difficulties that arise while training…
• How can we define computation graphs, and why are they significant in modern machine learning frameworks?
• How does Early Stopping function within deep learning, and why is it beneficial?
• How does the behavior of a neural network change when you reduce its layer width but increase its depth?
• In a neural network, what do hidden layers actually compute?
• In the realm of Neural Networks, how is a 1x1 convolution operation defined and why is it used?
• What are the key distinctions between a linear activation function and a non-linear activation function in a neural…
• What is the significance of applying non-linear activation functions in neural networks, and why is it essential to…
• Could you explain the concept of a Boltzmann Machine and its core ideas in machine learning?
• Under what circumstances should a Deep Recurrent Q-Network be employed?
• How would you compare and contrast the usage of Hidden Markov Models and Recurrent Neural Networks for tasks…
• What factors underlie the greater effectiveness of deeper neural networks compared to shallower ones?
• What different strategies exist for adding skip connections in a neural network?
• How do Region-Based CNN (R-CNN), Fast R-CNN, and Faster R-CNN differ from each other?
• In what ways do deep neural networks overcome or alleviate the problems typically referred to as the "curse of…
• How can we apply Ensemble Techniques in conjunction with deep neural networks to enhance model performance and…
• Why is Logistic Regression frequently referred to as a linear model?
• How can we arrive at a mathematical understanding of how Logistic Regression works?
• Which techniques can help minimize overfitting in a logistic regression model?
• How would you thoroughly examine the space complexity of Logistic Regression?
• How would you implement logistic regression in a fully vectorized manner?
• How can you generate a classification prediction using a Logistic Regression approach?
• How would you explain the concept of a learning rate in a straightforward way, including how it impacts the training…
• How can the discrepancy between actual and predicted values be measured in a linear regression framework?
• How can you verify if a linear model is overfitting its training data?
• How does Non-Linear Regression differ from Linear Regression in terms of modeling and assumptions?
• What approaches can be used to verify that a regression model appropriately fits the dataset?
• Explain the main distinctions between a Linear Regression model and a Decision Tree model for regression tasks
• How can missing data in regression scenarios be effectively managed?
• Under what circumstances might you opt for normalization over standardization in linear regression, and vice versa?
• How do we use hypothesis testing in linear regression models?
• Could you clarify the role and meaning of the intercept term in a regression model?
• What are some common metrics to assess the performance of a regression model, and in which scenarios would each…
• What is one limitation of the R-squared metric, and how could it be handled or resolved?
• What are the advantages of using Root Mean Squared Error instead of Mean Absolute Error for evaluating model…
• How do regression-based models differ from ANOVA in a statistical framework?
• How can you address the problem of overfitting in Linear Regression models?
• How do you identify the presence of collinearity, and how would you describe multicollinearity?
• How can you verify whether a linear regression model satisfies all the usual assumptions needed for valid regression…
• Provide an intuitive understanding of how RANSAC Regression operates
• What is the conceptual motivation behind the Gradient Descent process?
• How can we interpret the linear system Ax = b, and under what circumstances does it possess a single unique solution?
• How do the dot product and cross product differ from each other in the context of vectors?
• How can you define and compute the Frobenius norm of a matrix?
• Under which circumstances can a diagonal matrix be inverted?
• How can we determine the normal vector for the given surface S described below?
• How can we determine whether two linear equations in two variables have a unique solution, infinitely many solutions…
• How would you define positive-definite, negative-definite, positive-semidefinite, and negative-semidefinite matrices?
• What does it mean for a matrix to be Orthogonal, and why is this property beneficial from a computational…
• Under which circumstances does a matrix possess an inverse?
• How would you explain the distinction between a matrix and a tensor, and how do they fundamentally differ?
• How would you define the Hadamard product of two matrices, and what does it look like mathematically?
• Determine whether the following matrix can be diagonalized, and if so, demonstrate how to perform the diagonalization
• How would you go about diagonalizing a matrix?
• Is it valid to treat the count of a vector’s nonzero entries as a norm? If not, provide the reasoning.
• How can we quantify or measure the magnitude of a vector in different ways?
• Which fundamental properties must a function fulfill in order to qualify as a norm?
• Explain how the span of a set of vectors is defined and elaborate on the concept of linear dependence.
• How can you use a vectorized method to assign values to the diagonal of a matrix in MATLAB?
• What is the significance of ensuring the data is mean-centered and standardized before applying Principal Component…
• Explain the meaning of the determinant for a square matrix and outline how to compute it
• What is the effect on a vector z when it is transformed by a positive definite matrix?
• Is the eigendecomposition of a real matrix always guaranteed to be unique? If not, how can we represent it?
• How would you describe the idea of broadcasting in the context of Linear Algebra?
• Why is it often preferable to rely on Singular Value Decomposition instead of just using Eigendecomposition?
• When is L1-norm regularization more advantageous compared to L2-norm regularization?
• Can you explain the idea behind singular eigenvalues, along with left singulars and right singulars, in linear…
• When would you apply the Moore-Penrose pseudoinverse, and in what way can it be computed?
• How can one derive the Singular Value Decomposition for a matrix M?
• In what way can you determine the eigenvalues for a given matrix, and would you demonstrate a practical example?
• What criteria can be used to determine when to stop iterating in k-Means clustering?
• How would you describe the detailed procedure used in the k-means clustering method?
• What phenomenon is described by the "Uniform Effect" in k-Means Clustering?
• How can you efficiently apply k-Means clustering when working with extremely large datasets?
• What differentiates standard K-Means clustering from Spherical K-Means clustering?
• In which types of situations does k-means clustering perform poorly?
• What purpose does Fuzzy C-Means Clustering serve in data analysis, and in what situations is it most beneficial?
• What specific cost function does k-Means attempt to minimize, and how is it formulated?
• How do k-Means and k-Medians differ, and under which conditions is one approach more appropriate than the other?
• When k-Means is run multiple times on the same dataset, how can we assess if the resulting cluster assignments stay…
• What steps are essential when preparing your dataset prior to employing k-Means clustering?
• How can entropy serve as a criterion for assessing the quality of clusters?
• How would you describe a consensus clustering method that uses k-Means as its foundation?
• What is the mechanism behind Spherical k-Means when handling data with very high dimensionality, particularly text…
• How can you methodically choose the ideal number of clusters k using the Silhouette Method?
• How do the resulting clusters differ when comparing standard k-Means to Mini-Batch k-Means?
• What are some key characteristics of the distance from a point to its centroid?
• In what scenarios would correlation-based distances be advantageous when performing k-Means clustering?
• Explain how Forgy Initialization, Random Partition Initialization, and the k-means++ Initialization strategy differ…
• In which scenarios would you opt for Fuzzy C-Means over k-Means for clustering tasks?
• How do we determine whether a dataset exhibits enough separability for clustering methods to provide meaningful…
• How can you figure out the right number of clusters to use when applying the K-Medoids technique?
• Why is the distance approach used by k-Medoids often deemed superior to that of k-Means?
• How are k-Means clustering and Principal Component Analysis connected to one another?
• What is the primary reason that k-Means typically relies on Euclidean distance as its preferred measure?
• What are the key benefits of using Gradient Descent instead of the standard Ordinary Least Squares method for linear…
• In the context of optimization tasks that may be convex or non-convex, does the gradient in stochastic gradient…
• Explain, using mathematical reasoning, why Stochastic Gradient Descent is faster than full-batch Gradient Descent.
• How does Adam’s optimization method differ from basic Stochastic Gradient Descent?
• Is Gradient Descent guaranteed to reach an optimal solution under all circumstances?
• Explain the distinctions between Mini-batch Gradient Descent, Stochastic Gradient Descent, and Batch Gradient…
• How can you contrast Batch Gradient Descent and Stochastic Gradient Descent, highlighting their key differences…
• How does the Adam optimizer, an extension of Stochastic Gradient Descent, operate in practice?
• How do you decide when to stop Gradient Descent during neural network training?
• What influence does the chosen batch size have on SGD's convergence properties, and what fundamentally drives this…
• Under what circumstances is it preferable to utilize optimizers like Adam, rather than standard stochastic gradient…
• How do gradient-based methods handle relatively flat areas in the optimization landscape, particularly if they…
• Which variations of Gradient Descent do you know and in what scenarios might they be applied?
• What sets Maximum Likelihood Estimation apart from Gradient Descent?
• Is it feasible to employ gradient-based optimization methods for cost functions that are not strictly convex?
• How do you use the F-test to select features?
• Explain One-Hot Encoding and Label Encoding. Does the dimensionality of the dataset increase or decrease after…
• How would you decide on the importance of variables for the Multivariate Regression model?
• What methods to perform Feature Engineering from text data do you know?
• How do you perform Principal Component Analysis (PCA)?
• How do you transform a Skewed distribution into a Normal distribution?
• How do you perform feature selection with Categorical Data?
• What Feature Selection methods do you know?
• How do you perform End of Tail Imputation?
• When would you remove correlated variables?
• How has Translation of words improved from the Traditional methods?
• How would you improve the performance of Random Forest?
• Why would you use Permutation Feature Importance and how does this algorithm work?
• How does the Recursive Feature Elimination (RFE) work?
• Explain the Stepwise Regression technique
• What's the difference between Forward Feature Selection and Backward Feature Selection?
• Why is it that, despite ensemble methods frequently achieving superior results, they are not universally used in…
• Does removing outliers undermine the core advantages of using Ensemble Learning methods?
• How would you describe a Super Learner Algorithm, and what is its fundamental idea in ensemble learning?
• Is a Random Forest considered an ensemble-based method?
• How do ensemble-based methods support incremental learning over time?
• How is a Super Learner ensemble framework structured, and what are its main building blocks?
• In which cases might you choose not to apply ensemble methods for classification tasks?
• Projecting LDA Centroids Below K-1 Dimensions Using Eigendecomposition
• Demystifying Logistic Regression Coefficients: Understanding Log-Odds and Odds Ratios
• Logistic Regression Coefficients via Maximum Likelihood Estimation.
• Geometric Probability: Forming a Triangle from Random Stick Breaks
• Gaussian Mixture Models for Fraudulent Transaction Anomaly Detection
• What differentiates homoskedastic from heteroskedastic residuals, how to detect and address measurement error-induced…
• Why do we refer to Logistic Regression as a regression method rather than a classification technique?
• What makes the binary cross-entropy loss used in logistic regression convex in its parameters?
• How would you apply logistic regression, under the umbrella of supervised learning, to perform classification tasks?
• How do the Softmax function and the Sigmoid function differ from each other?
• In what way do we carry out the training process for a Logistic Regression model?
• Why is it not appropriate to use a linear regression model in place of logistic regression for classification tasks?
• K-Means Centroid Updates Using Batch and Stochastic Gradient Descent.
• Objective Function Derivation for Linear Regression Under Gaussian Input Noise
• How would you verify a user's claimed high school before granting them a school-logo Instagram sticker?
• How can we use ML to match user queries with relevant answers from an existing FAQ list?
• How would you reliably forecast next year's revenue as part of a tech company's prediction team?
• How would you enhance the overall user experience for Uber Eats, and which primary factors or metrics would you…
• How would you assess an app’s success and isolate engagement from celebrity’s natural interaction tendencies?
• How can we determine whether a newly introduced feature, which advises dashers on the best times to be online to…
• How can you adjust output probabilities after training on a downsampled imbalanced binary classification dataset?
• Imagine you have built a new model to predict how long it will take for food deliveries to arrive. How can you verify…
• What five key metrics would you track to assess Google Docs' overall performance and health?
• How would you explain Naive Bayes vs. Logistic Regression and recommend one for spam classification?
• How would you build an ETL workflow that ingests video material and consolidates unstructured information derived…
• How would you design a multi-output pipeline to convert resumes (images/PDFs) into searchable text?
• How would you select the top 1,000 businesses from 100,000 for outreach as a credit card company?
• Suppose you are managing online groups (like Facebook Groups) and want to significantly boost the number of comments…
• Suppose you’re constructing a classification model and want to reduce overfitting issues in tree-based methods. How…
• As a Facebook data scientist, how would you reduce harmful ads despite their high revenue contribution?
• How can you structure a rewards plan so that ride-hailing drivers gravitate towards densely populated city zones…
• Suppose we intend to create a new algorithm for Lyft Line.How could we run appropriate tests to validate its…
• How would you detect fraudulent Amazon accounts and scale data collection with minimal human oversight?
• Suppose a colleague suggests creating a novel game functionality for Google Home. How would you determine whether…
• How would you compare your new search system to the current one and track performance metrics?
• How would you assess the reliability of an A/B test result with a p-value of 0.04?
• You have a large number of hypotheses to evaluate and plan to conduct multiple t-tests. What factors do you need to…
• In an A/B testing setup, what steps can be taken to confirm that participants are truly assigned randomly to each…
• How would you explain loan rejections from a binary classifier without access to feature weights?
• What formula would you use to calculate average lifetime value given $100/month, 10% churn, 3.5 months retention?
• What basic premises underlie linear regression, and how do these premises guide the correct application of the model?
• We are tasked with estimating Airbnb rental prices. Out of linear regression and random forest regression, which one…
• What metrics assess ride demand over time, identify excess demand, and define when demand is too high?
• How would you automate detection of gun sale listings on a firearm-restricted marketplace website?
• How would you determine if the email redesign caused the conversion rate increase from 40% to 43%?
• How would you select 10,000 early users and assess overall performance for a new show launch?
• Which Facebook interface areas would you target and what strategies would you use to boost Instagram usage?
• Why would comment threading increase per-user comments by 10% but reduce new posts by 2%, and what metrics validate…
• How would you design a system to detect fraud and notify customers via text for confirmation?
• How would you build a model to predict optimal bids for unseen keywords using keyword and bid data?
• How would you compute a precision metric to evaluate if higher-rated results appear in earlier positions?
• What potential biases could affect Jetco’s boarding time study, and how would you investigate them?
• How can we measure how teens' engagement on Facebook shifts once their parents become members of the platform?
• How can we assess if one million Seattle ride records are enough for reliable arrival time prediction?
• How do you handle 20% missing square footage in 100,000 Seattle home sales for price prediction?
• What key metrics would you track on a company-wide dashboard for a DTC sock e-commerce platform?
• How would you design a dynamic pricing system for Airbnb, considering demand and available listings?
• How would you discuss the bias-variance tradeoff when selecting the final model for a loan-granting system?
• What insights can be drawn from daily message counts, their distribution, and user-started conversations in 2020?
• Which activation function—ReLU or Tanh—would you choose for classifying chair categories, and why?
• Photo posts on Facebook composer dropped from 3% to 2.5%—how would you investigate the cause?
• Why is it important to evaluate and measure bias in meal preparation time prediction models?
• How would you design an A/B test and use bootstrap sampling to get confidence intervals for conversion rates?
• Implement K-Means clustering from scratch in Python and return cluster labels for each data point.
• How would you determine the value of renewing a popular sitcom’s license on Netflix?
• How does the Adam optimizer differ from other optimizers and what specific advantages does it offer?
• Should you split the boosting model by user age group to predict subscription conversion likelihood? Why?
• Using a query, how do user interactions (likes/comments) correlate with higher purchase rates?
• How would you query to visualize unsubscribe impact on login behavior over time using event and bucket data?
• Imagine a default risk model where its recall is high but its precision is relatively low. What implications could…
• Why might the identical machine learning method yield varying levels of success even if the same dataset is used?
• Suppose you have a categorical feature containing thousands of unique categories. What methods would you use to…
• How would you design control and test groups to evaluate a new “close friends” feature on Instagram Stories, ensuring…
• How would you decide which proposal best boosts DAU, and what data or metrics would guide you?
• How would you efficiently retrieve top 10 similar jobs by title and description from millions daily?
• How would you use participant ratings of 100 new TV pilots to prioritize them on a streaming platform?
• Why does standard k-Means always converge in finite steps? Outline the proof and required assumptions.
• How would you design an A/B test to allocate budget effectively across new marketing channels?
• How would you evaluate a feed-ranking algorithm if some metrics improve while others decline?
• Can you write a query to test if frequent job changers reach data science manager roles faster?
• How would you detect and query users creating multiple accounts to upvote their own comments?
• Which factors would you consider significant for a push notification experiment, and should it be broadly rolled out?
• What insights can be derived from multi-select political survey data where respondents can choose multiple…
• How can we investigate the impact of extra push notifications on overall user engagement and unsubscribes?
• Which clustering methods are ineffective for datasets with both continuous and categorical features, and why?
• How would you address Facebook's finding that more friends lead to less post activity in "people you may know"?
• How do kernel methods work in ML, what makes a matrix a valid kernel, and what happens if criteria are violated in…
• How would you evaluate if enabling Instagram's messaging system to interact with third-party services is worth…
• What key metrics should be tracked to detect and prevent fraud, and how do they improve security?
• How would you design a refund policy balancing customer goodwill and company revenue at a food startup?
• How would you design an occupancy forecasting model, gather training data, and evaluate its performance?
• How would you measure and report uncertainty in stock price forecasts using historical predictions and actual values?
• How would you measure Uber Eats’ success and validate its overall impact on Uber’s business?
• How would you estimate incorrect pickup pin frequency using only user location data at Uber?
• How would you evaluate the viability of Facebook adding peer-to-peer payments in Messenger like Venmo?
• How would you assess, implement, and measure the success of a 50% ride-sharing discount promotion?
• Which model would best predict agent needs across call centers, and how would you evaluate and balance accuracy?
• How would you infer a cardholder’s residence from spending records for fraud detection?
• How would you determine the fee-free cancellation wait time for a ridesharing customer?
• How would you design and implement a "trending posts" feature to boost engagement at Reddit?
• How can we scale model training to handle Netflix's millions of titles and users in a recommendation system?
• How would you evaluate your clustering of new players without labeled data, based on play styles?
• How would you improve your CNN to handle mislabeled pug/pit bull data and tough conditions like fog?
• How would you validate if human-rated relevance scores influence click-through rate using Facebook search logs?
• How would you design a ML system to reduce missing or incorrect orders at DoorDash?
• How would you build a job recommendation feed using LinkedIn data, applications, and user-provided answers?
• What metrics would you analyze to assess if only top-tier creators now succeed on YouTube?
• How would you evaluate a 30-day free trial’s effectiveness in driving new Netflix subscriber acquisition?
• How would you analyze a non-normal distribution in a small Uber Fleet A/B test and determine the winner?
• If a key feature's decimal point was dropped (e.g., 100.00 → 10000), is your model invalid, and why?
• We need to add a “green icon” showing an active user in a chat system but cannot do an AB test before launch. How…
• How would you build a specialized search platform for podcasts, utilizing both their transcripts and associated…
• How can a media company that profits from monthly subscriptions assess the effect on Customer Lifetime Value if they…
• When are regularization methods and cross-validation most appropriately applied to boost a machine learning model’s…
• You are part of Meta's team, and you need to devise a plan to expand Meta’s product offerings in a growing, yet…
• Suppose there's a national park with many deer living both inside its boundaries and in nearby areas. How can one…
• How would you analyze whether Netflix’s subscription cost is genuinely the primary factor influencing a consumer’s…
• In what way would you set up a model to predict the most effective point in a video for placing a commercial break?
• How can one devise an ML-driven solution that automatically curates a user-specific weekly playlist, similar to…
• Imagine you're working at a rideshare company like Lyft & you plan to test 3 different cancellation fees. How would you determine which fee is the best choice to adopt?
• Imagine you are a Product Data Scientist at Instagram aiming to gauge how well Instagram TV is doing. What metrics…
• How might one design a real-time type-ahead recommendation system for Netflix as users start typing their queries?
• Which loss function does k-means minimize, and what are the centroid update formulas for batch and SGD?
• Why does maximizing likelihood under normally distributed errors equal minimizing the sum of squared errors in linear…
• Explain the core concept of PCA, its matrix formulation and derivation, iterative procedure, and constrained…
• Would applying a square root to a classifier’s scores alter the ROC curve, and what transformations would?
• Suppose X is a one-dimensional Gaussian random variable with mean μ and variance σ². How can we find its differential…
• How would you create a system to predict a user’s likelihood of buying a specific item? Also discuss the method’s…
• Compare and contrast Gaussian Naive Bayes (GNB) and logistic regression as classification methods, and discuss the…
• Explain how the kernel trick operates in SVMs, illustrate with a straightforward example, and discuss your approach…
• How would you go about creating a model to predict when a Robinhood user is likely to stop using the platform?
• Explain the standard logistic regression formulation for binary classification, and outline how the log-likelihood is…
• How would you design a music recommendation solution that generates a 30-track personalized weekly playlist for each…
• Show how to derive the variance-covariance matrix of the Ordinary Least Squares parameter estimates using a…
• What are the differences between minimizing squared error vs. absolute error, and when is each suitable?
• If predictors are correlated in multiple linear regression, how does it affect outcomes and how to address it?
• How would you handle missing values in a large payment transaction dataset for fraud prediction?
• After poor logistic regression performance, what improvements or alternative methods would you consider for better…
• Is 10,000 delivery records from a Singapore beta test sufficient to build an accurate ETA model?
• How can we explain loan denials without directly examining the model’s feature weights in binary classification?
• Assume we have N measurements from a single variable that we assume follows a Gaussian distribution. How do we find…
• How would you enhance the resilience of a model when dealing with outliers?
• Explain what motivates the use of Random Forests, and describe two key ways they offer improvements over a single…
• While using K-means clustering, how can we decide on the optimal value of k (number of clusters)?
• Explain how gradient boosting compares with random forests in terms of their strategies, structure, and typical…
• Suppose you have a massive collection of text data. What process would you follow to detect words that are…
• Explain the bias-variance trade-off in machine learning and show how to represent it with an equation.
• Explain how cross-validation is carried out and why it is beneficial in practice.
• How would you develop a lead scoring framework to predict whether a prospective business will upgrade to an…
• How would you build a system that recommends music tracks to users?
• Explain what characterizes a function as convex. Then provide a concrete example of a non-convex machine learning…
• Explain the concept of entropy and information gain in decision trees, and illustrate them with a concrete numeric…
• Explain the concept of L1 and L2 regularization in machine learning, and discuss how they differ from each other
• How would you handle a binary classification task with a 99%-1% significant class imbalance?
• Explain how gradient descent works and discuss why stochastic gradient descent is often preferred
• Identifying Synonyms in Large Corpora Using Word Embeddings
• ROC Curve Invariance: Effect of Monotonic Transformations Like Square Root on Scores.
• Evaluating Metrics via Experimentation on Complex Multi-Sided Marketplace Platforms.
• Addressing Correlated Predictors (Multicollinearity) in Regression using Regularization and PCA.
• Regression to the Mean: Why Credit Model Cutoffs Overestimate Actual Creditworthiness
• Essential Metrics for Evaluating Fraud Detection Binary Classifiers
• Predicting User Churn with Machine Learning: Classification Models and Feature Engineering