Fundamentals of Machine Learning — Winter 2024

List out the different issues in machine learning.

Differentiate Human Learning & Machine Learning.

Explain Supervised Learning in detail.

Explain the use of the candidate elimination algorithm.

Differentiate Gradient Descent and Stochastic Gradient Descent.

What is semi-supervised learning? Explain the steps of the self- training algorithm.

List out the characteristics of an artificial neural network and explain them in brief. Also discuss various problems for neural network learning.

Differentiate Pre-pruning and Post-pruning.

Explain inductive bias with a suitable example.

What is the perceptron? What is the perceptron training rule? What are its limitations and by which rule these limitations can be overcome?

What is a use of the Cost function in ANN?

Explain Under-fitting and Over-fitting with an example.

Explain the concept of EM Algorithm.

What is threshold activation function in ANN?

Discuss application of ANN in face recognition.

Explain Decision Tree in details.

For which type of problem characteristics, decision tree algorithm is suitable?

Briefly explain the properties of Gini impurity in a decision tree.

What is the back propagation algorithm? Explain it in detail. What are the limitations of this algorithm?

What is the Least Square Error Hypothesis? Explain it in brief.

Explain Hypothesis and Hypothesis Space Search in brief.

Describe the structure of an artificial neuron. How is it similar to a biological neuron? What are its main components?

What is recommender system? How machine learning methods can be useful in it?

Explain Bayes Optimal classifier.

Explain Naïve Bayes classifier with an example.