a) Explain the difference between classification and regression models. (Unit 1)

b) What a hypothesis space represents in machine learning? List some basic characteristics of a hypothesis space? (Unit 1)

a) Discuss some common limitations of machine learning. (Unit 1)

b) How do neural networks leverage parallel processing to perform computations efficiently? (Unit 2)

a) What is the Perceptron learning algorithm? Discuss how the Perceptron learning algorithm adjust the weights of connections between neurons to learn from data? (Unit 2)

b) Describe how neural network architectures are represented graphically or conceptually in machine learning? (Unit 2)

a) Explain the concept of entropy in the context of Decision Trees. How does entropy affect the construction and splitting of nodes in a Decision Tree? (Unit 3)

b) Explain the difference between linear and logistics regression with example. (Unit 3)

a) List the advantages of Support Vector Machine (SVM) and how optimal Hyper plane differ from Hyper plane. (Unit 3)

b) Describe the basic concept of K-means clustering and how it partitions data into K clusters based on similarity. (Unit 4)

a) How does adaptive hierarchical clustering adaptively adjust the number of clusters based on the data? Discuss. (Unit 4)

b) Explain the difference between the E-step and M-step in the EM algorithm. How does EM handle missing or incomplete data in probabilistic models? (Unit 4)

a) List the fundamental steps involved in designing and conducting a machine learning experiment. (Unit 5)

b) What is cross-validation in machine learning? Describe its purpose and how it helps in evaluating the performance of machine learning models. (Unit 5)

Write a short note on any two of the following.

a) Partial Least Squares (Unit 1)

b) Activation functions (Unit 2)

c) Goals of clustering (Unit 4)

d) Measuring classifier performance (Unit 5)