Describe Machine Learning and highlight its key differences from traditional programming Methods.

Explain the main difference between Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) in reducing dimensions.

Write a note on Reinforcement Learning.

What is a logical model in the context of Machine Learning?

What distinguishes unsupervised learning from supervised and semi-supervised learning techniques?

Explain Grouping and Grading models in a machine learning with example?

Elaborate decision tree regression and random forest regression.

Differentiate between multivariate regression from univariate regression?

Explain bias-variance trade-off with neat diagram.

Which one of these is Underfit or Overfit? Why? Comment with respect to Bias and Variance.

Explain any two evaluation metrics in regression model.

List and Explain any two different types of Regression.

Describe Machine Learning and differentiate it from traditional programming.

Explain Principal Component Analysis used in Machine Learning.

Explain the relationship between Artificial Intelligence, Machine Learning and data science.

Explain types of Machine Learning.

Explain Linear Discriminant Analysis (LDA) used in Machine Learning.

Differentiate Grouping and Grading models of Machine Learning.

Explain three evaluation metrics used for regression model.

Explain the Random forest Regression.

Differentiate between Regression and Correlation.

What is Regression? Explain types of Regressions.

Explain Bias-Variance Trade-off with respect to Machine Learning.

Differentiate Ridge and Lasso Regression techniques.

Compare Machine Learning with Traditional programming.

What is Dimensionality Reduction, Explain any one Dimensionality Reduction technique.

Write a note on Reinforcement Learning.

Explain parametric & nonparametric models in machine learning.

Differentiate supervised and unsupervised learning techniques.

Elaborate grouping and grading models.

Elaborate random forest regression.

Differentiate multivariate regression and univariate regression.

Define Regression. Explain types of regression.

What is underfitting and overfitting in machine Learning explain the techniques to reduce overfitting?

Explain any two Evaluation Metrics for regression.

Explain Elastic Net regression in Machine Learning.

What are kernel functions in SVM? Describe the Radial Basis Kernel, and Sigmoid kernel.

Write formula for accuracy, precision, recall and f1-score. Calculate accuracy, precision, recall and f1-score for given example. Actual Values (Cancer, No Cancer) Predicted Values (Cancer, No Cancer)

Explain any 4 evaluation measures of Multiclass classification.

Differentiate Balanced and Imbalanced Classification.

Write a detailed note on K Nearest Neighbour algorithm with suitable example.

Differentiate Agglomerative Hierarchical Clustering and Divisive Hierarchical Clustering.

What is clustering? Elaborate Types of Clustering.

Explain DBSCAN algorithm with advantages and disadvantages.

Describe centroid based clustering algorithm and explain any one type with example.

Explain Following with respect to Ensemble learning. i) Need of Ensemble Learning ii) Advantages of Ensemble methods iii) Ensemble learning limitations

Elaborate stacking approach of ensemble with example.

Describe ensemble learning. Explain Gradient Boosting ensemble learning techniques.

Explain any three voting mechanism in ensemble learning.

Differentiate supervised and unsupervised learning with example.

Explain Reinforcement Learning need and its types in detail?

Explain following terms: i) Belman Equation ii) Markov Chain iii) Q table iv) Q function

How does the Markov property relate to Reinforcement Learning? Why is it important?

Apply K-Nearest Neighbor Algorithm (KNN) on following data. Predict the student result for values physics = 6 marks, Chemistry = 8 marks. Consider number of neighbours K = 3 and Euclidean Distance as distance measure. Physics (marks) Chemistry (marks) Results 4 3 Fail 6 7 Pass 7 8 Pass 5 5 Fail 8 8 Pass

Explain support Vector Machine classification algorithm with suitable example.

Explain any 4 evaluation measures of Binary classification with example?

Explain construction of multi-classifier. i) One Vs. All approach ii) One Vs One approach

Differentiate between Binary - vs - Multiclass Classification.

Explain K - Means clustering algorithm and states the advantages and disadvantages of k-means clustering algorithm.

Explain Gaussian mixture model with example.

Elaborate need of clustering and explain how the elbow method is used to decide the value of cluster k.

Explain Divisive Hierarchical clustering (DHC) algorithm with example.

Differentiate the Bagging and Boosting approach of ensemble learning.

Explain different types of voting mechanisms in ensemble learning.

Explain AdaBoost algorithm in detail.

Compare Homogeneous and Heterogeneous ensemble methods.

What is the ensemble learning? Explain any two ensemble learning techniques.

Explain random forest ensembles with an example.

Explain following terms: i) Markov Property ii) Bellman Equation iii) Markov Reward Process iv) Markov Chain

Explain Q-Learning algorithm with an example.

What is Reinforcement Learning? Explain the real time applications of reinforcement learning.

Explain following terms : i) Supervised Learning. ii) Unsupervised Learning. iii) Reinforcement Learning.