Question 1

What is Reinforcement Learning?

Accepted Answer

Reinforcement Learning is a behavioral mathematical framework where an Agent learns to make optimal sequential decisions in an Environment purely by maximizing a numerical Reward signal.

Question 2

Define precision and recall.

Accepted Answer

Precision measures the mathematical accuracy of positive predictions (True Positives / Predicted Positives), while Recall measures the ability to detect all actual positive instances (True Positives / Actual Positives).

Question 3

What is the role of a learning rate in gradient descent?

Accepted Answer

The learning rate is a critical mathematical hyperparameter in Gradient Descent that strictly dictates the absolute magnitude of the step taken toward the global minimum of the loss function.

Question 4

Differentiate between L1 and L2 regularization.

Accepted Answer

L1 regularization mathematically forces absolute feature selection by driving weights exactly to zero, while L2 regularization smoothly penalizes massive weights without completely eradicating them.

Question 5

What is one-hot encoding?

Accepted Answer

One-hot encoding is an absolute architectural requirement for mathematically translating categorical string variables into orthogonal binary vectors that a machine learning algorithm can process.

Question 6

Define a perceptron.

Accepted Answer

A perceptron is the absolute fundamental mathematical unit of a neural network; it calculates the weighted sum of inputs, adds a bias, and applies a step activation function.

Question 7

What is the Silhouette score used for?

Accepted Answer

The Silhouette Score is a strict mathematical metric used exclusively in unsupervised clustering to evaluate how dense and well-separated the generated clusters are.

Question 8

Name two common activation functions.

Accepted Answer

Two absolute dominant activation functions are the Sigmoid function (used for binary probability) and the Rectified Linear Unit (ReLU) function (used aggressively in deep hidden layers).

Question 9

What is the naive assumption in Naive Bayes?

Accepted Answer

The naive assumption is the strict, mathematically flawed presumption that all input features are absolutely conditionally independent of one another given the target class.

Question 10

What is the curse of dimensionality?

Accepted Answer

The curse of dimensionality is a catastrophic mathematical phenomenon where adding more features exponentially increases the volume of the data space, rendering distance metrics completely meaningless.

Question 11

Explain Ridge and Lasso regularization techniques.

Accepted Answer

A definitive mathematical comparison of Ridge (L2) and Lasso (L1) regularization techniques. Details how adding penalty terms to the loss function aggressively prevents catastrophic model overfitting and manipulates feature weights.

Question 12

Describe the Naive Bayes classifier theorem and its applications.

Accepted Answer

An advanced theoretical breakdown of the Naive Bayes Classifier. Executes the mathematics of Bayes' Theorem, details the horrific yet highly effective conditional independence assumption, and highlights its absolute dominance in NLP applications.

Question 13

Explain Agglomerative Hierarchical Clustering with an example.

Accepted Answer

A detailed algorithmic trace of Agglomerative Hierarchical Clustering. Explains the "bottom-up" mathematical fusion of data points using distance linkages to generate a massive tree-like Dendrogram architecture.

Question 14

Discuss various methods to deal with highly imbalanced datasets.

Accepted Answer

A critical engineering review of handling Imbalanced Datasets. Violently analyzes the failure of accuracy metrics, and details the mathematical execution of SMOTE (Oversampling) and strict Algorithmic Penalization (Class Weights).

Question 15

What is a ROC curve and how is the AUC calculated?

Accepted Answer

A rigorous exposition on the ROC Curve and AUC metric. Details how graphing True Positive Rate against False Positive Rate mathematically evaluates binary classifiers independently of threshold selection.

Question 16

Explain the architecture of a Multilayer Perceptron (MLP).

Accepted Answer

An architectural breakdown of the Multilayer Perceptron (MLP). Violently details the strict mathematical feedforward propagation of inputs through dense hidden layers utilizing non-linear activation functions to solve complex non-linear problems.

Question 17

Discuss the difference between Standardization and Normalization for feature …

Accepted Answer

A definitive mathematical comparison of Feature Scaling architectures. Contrasts the strict $0$ to $1$ bounding of Normalization against the Z-score variance alteration of Standardization required by gradient descent algorithms.

Question 18

Elaborate on the architecture and working of Convolutional Neural Networks (C…

Accepted Answer

A massive architectural exposition on Convolutional Neural Networks (CNNs). Violently details the mathematical execution of Convolutional layers, feature maps, ReLU activation, Max Pooling downsampling, and the final Dense classification layers.

Question 19

Explain K-Means vs Hierarchical clustering. Give a detailed mathematical expl…

Accepted Answer

An exhaustive theoretical comparison of K-Means and Hierarchical Clustering. Violently breaks down the Expectation-Maximization mathematics of K-Means centroids against the linkage-based distance matrices of Agglomerative trees, highlighting their absolute computational complexity differences.

Question 20

Discuss Logistic Regression cost function derivation and how gradient descent…

Accepted Answer

A rigorous mathematical derivation of the Logistic Regression Cost Function. Violently details the failure of Mean Squared Error, the necessity of Maximum Likelihood Estimation to derive Log-Loss, and the exact Gradient Descent calculus required to minimize it.

Question 21

Provide a detailed explanation of Hidden Markov Models (HMM) and their applic…

Accepted Answer

A massive architectural breakdown of Hidden Markov Models (HMM). Explains the strict mathematical separation of hidden state transitions and observable emissions, detailing the execution of the Viterbi Algorithm to deduce the most probable sequence of hidden realities.

Question 22

Explain the concept of Q-Learning in Reinforcement Learning with a detailed a…

Accepted Answer

An exhaustive algorithmic exposition on Q-Learning in Reinforcement Learning. Violently details the strict mathematical update of the Q-Table using the Bellman Equation to balance immediate rewards against discounted future expectations.

Machine Learning