Differentiate between overfitting and underfitting in machine learning models.
How do precision and recall differ, and when would you prioritize one over the other?
What is the difference between classification and regression problems in machine learning?
What is clustering? Give an example.
What is the k-means algorithm?
What is the difference between K-Means clustering and hierarchical clustering?
Compare the confusion matrix components: TP, FP, TN, FN.
What is the difference between supervised and reinforcement learning?
Define dimensionality reduction with an example.
What is PCA in machine learning?
Derive the Simple Linear Regression Equation with an example
𝐴𝑐𝑡𝑢𝑎𝑙= [10,20,30], 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑= [12,18,29] Actual= [10,20,30], Predicted= [12,18,29].
Describe the process and calculate the mean squared error (MSE) for the following predictions:
Explain the difference between 'Type I' and 'Type II' errors in the context of hypothesis testing.
Illustrate your answer with an example related to machine learning.
Calculate the mean and standard deviation for the data set: 3, 5, 7, 9, 11
Derive the Logit Equation in Logistic Regression.
Describe Root Mean Squared Error (RMSE) and its significance in model evaluation
Compare and contrast MSE, MAE, and RMSE in terms of their sensitivity to outliers in data.
What is a Perceptron and how does it function in neural networks?
Describe the architecture and learning process of an Artificial Neural Network (ANN) including
forward and backward propagation. Include a numerical example of forward propagation using a
simple network structure.
What is reinforcement learning? Explain Q-learning with a suitable example and equations.
Explain the concept of gradient descent. How is it used in training machine learning models?
Discuss the bias-variance trade-off. How does it affect model performance?
What is ensemble learning? Compare and contrast bagging and boosting techniques with examples.
Describe the Scaled Exponential Linear Unit (SELU) and its benefits over traditional ReLU in
model performance.
Discuss the concept of cyclic learning rates and how they can help overcome challenges in training
deep learning models.
Compare and contrast sigmoid and hyperbolic tangent (tanh) activation functions, including their
effects on model training.
Explain how ensemble learning techniques like bagging and boosting differ in their approach to
reducing the variance and bias in predictions.
Describe the working of the k-Nearest Neighbors (k-NN) algorithm. How does the choice of
‘k’ affect its performance?
Explain Support Vector Machines (SVM). How does it separate data and what is the role of
the kernel function?
What is linear regression? Derive the formula for simple linear regression and explain how
the model is trained.
What is Principal Component Analysis (PCA)? Explain its purpose and how it is used for
dimensionality reduction.
Explain briefly how supervised, unsupervised learning, and reinforcement learning works.
Explain neural networks and how backpropagation works in training a network.
Explain briefly logistic regression.
What is overfitting in machine learning? What are the techniques to prevent it?
Explain briefly about Naïve Bayes classifier.
Explain the concept of decision trees. How are they constructed? Discuss entropy and information
gain with an example.
What are evaluation metrics in classification problems? Explain accuracy, precision, recall, and F1-
score with an example.
Describe the concept of model selection and cross-validation. Why is cross-validation important in
machine learning?
