Machine Learning — Winter 2021

Explain the following terms: - 1. Histogram 2. LOOCV 3. Kappa value 4. Elbow method 5. Dendogram 6. Neural Network 7. Sigmoid function

Explain in details, the different components of a box plot? When will the lower whisker be longer than the upper whisker? How can outlier be detected using box plot?

What is machine learning? Explain the different types of machine learning?

Explain qualitative and quantitative data in details.

Describe the structure of an artificial neuron. How is it similar to a biological neuron?

What is model in terms of machine learning? How can you train a model?

What is feature selection? What are the different approaches of feature selection?

Explain overfitting and underfitting in context of machine learning models. What are the major causes of it?

Explain feature construction in detail with example.

Explain Naïve Bayes algorithm with suitable example.

What is KNN algorithm? Explain advantages and dis-advantages of KNN algorithm

What are Bayesian Belief networks? Where are they used?

Discuss the decision tree algorithm in detail.

Explain linear regression model in detail with example.

Explain k-means and k-mediods with a neat diagram

Discuss the SVM model in detail with different scenarios.

Explain the Apriori algorithm for association rule learning with example.