List of questions and answers in detail:

1. What is Unsupervised Learning? Give an example. 2. Give 3 differences between classification and regression. 3. What is Empirical Risk Minimization? Explain Inductive Bias 4. What is Non-Uniform Learnability? 5. What are the approaches for Model selection? 6. Give the definition of Learning via Uniform Convergence

7. What is formal learning and explain the different stages involved? 8. Explain Probably Approximately Correct in detail.

9. Explain Simple Linear Regression with an example. Give the equations for squared loss, Mean Squared Error, and the cost function. 10. What is a Convex Function? Explain in detail what is Gradient Descent. Give the convergence algorithm and discuss the gradient descent optimization. 11. Explain Logistic Regression with an example. Derive the cost function from sigmoid to log loss. 12. What is bias, variance, underfitting, and overfitting? Explain the Bias-Variance Tradeoff with figures. 7. What is Boosting? Explain AdaBoost in detail with figures. 8. Explain cross validation and its workflow? Explain k-fold cross validation, and write the pseudocode of k-fold cross validation 9. Explain Ridge and Lasso Regression? 10. What is maximum likelihood? Discuss Softmax Regression?