Unsupervised Learning · Question Bank

How would you choose the value of k?
If the labels are known, how would you evaluate the performance of your k-means clustering algorithm?
How would you do it if the labels aren’t known?
Given the following dataset, can you predict how K-means clustering works on it? Explain.

Clustering

Q8. What are the limitations of K-means clustering?

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Clustering

Q9. How do you initialize the centroids in K-means?

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Clustering

Q10. Why is it important to run the K-means algorithm multiple times with different initial cluster assignments, and how is the best solution selected?

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Clustering

Q11. What does within-cluster variation depicts?

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Clustering

Q12. What is the convergence criteria in K-means?

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Clustering

Q13. What is Silhouette score, and How do we calculate it?

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Clustering

Q14. What are some applications of K-means clustering?

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Clustering

Q15. Can K-means handle categorical data?

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Clustering

Q16. How do you evaluate the quality of K-means clusters?

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Clustering

Q17. k-means and GMM are both powerful clustering algorithms.

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Compare the two.
When would you choose one over another?

Clustering

Q18. What are the key parameters in DBSCAN, and what do they represent?

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Clustering

Q19. What is the difference between core points, border points, and noise points in DBSCAN?

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Clustering

Q20. How does DBSCAN handle clusters of different shapes?

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Clustering

Q21. What are the advantages of using DBSCAN over other clustering algorithms, such as K-means?

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Dimensionality Reduction

Q22. Why do we need dimensionality reduction?

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Dimensionality Reduction

Q23. List down the two main approaches for dimensionality reduction?

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Dimensionality Reduction

Q24. What are some linear techniques of dimensionality reduction?

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Dimensionality Reduction

Q25. What is the benefit of using linear methods for dimensionality reduction?

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Dimensionality Reduction

Q26. What is the benefit of using linear methods for dimensionality reduction? (Part 2)

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Dimensionality Reduction

Q27. What are the drawbacks of using linear methods for dimensionality reduction?

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Dimensionality Reduction

Q28. What are the benefits of using nonlinear methods in dimensionality reduction?

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Dimensionality Reduction

Q29. What are some non-linear techniques of dimensionality reduction?

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Dimensionality Reduction

Q30. Eigen decomposition is a common factorization technique used for dimensionality reduction. Is the eigen decomposition of a matrix always unique?

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Dimensionality Reduction

Q31. Name some applications of eigenvalues and eigenvectors.

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Dimensionality Reduction

Q32. We want to do PCA on a dataset of multiple features in different ranges. For example, one is in the range $0-1$ and one is in the range $10 - 1000$. Will PCA work on this dataset?

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Dimensionality Reduction

Q33. Under what conditions can one apply eigen decomposition? What about SVD?

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What is the relationship between SVD and eigen decomposition?
What’s the relationship between PCA and SVD?

Dimensionality Reduction

Q34. How does $t-SNE$ (T-distributed Stochastic Neighbor Embedding) work? Why do we need it?

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Dimensionality Reduction

Q35. Is it good to use PCA as a feature selection method?

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Dimensionality Reduction

Q36. Is PCA a linear model or non-linear model?

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Dimensionality Reduction

Q37. What is the importance of eigenvalues and eigenvectors in PCA?

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Dimensionality Reduction

Q38. How do you decide the number of principal components to retain in PCA?

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Dimensionality Reduction

Q39. What is the difference between PCA and Linear Discriminant Analysis (LDA)?

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Dimensionality Reduction

Q40. What are the limitations of PCA?

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Dimensionality Reduction

Q41. Explain the concept of whitening in PCA.

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Recommendation Engines

Q42. Given this directed graph.

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Construct its adjacency matrix.
How would this matrix change if the graph is now undirected?
What can you say about the adjacency matrices of two isomorphic graphs?

Recommendation Engines

Q43. What is a recommendation system?

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Recommendation Engines

Q44. What is the importance of recommendation system?

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Recommendation Engines

Q45. Define Items, Queries and Embedding in context of recommendation system?

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Recommendation Engines

Q46. What are the main components of a recommendation system?

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Recommendation Engines

Q47. What is candidate generation in a recommendation system, and what are the main approaches to accomplish it?

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Recommendation Engines

Q48. How do content-based and collaborative filtering methods utilize embedding spaces to represent items and queries?

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Recommendation Engines

Q49. Define similarity measures in context of recommendation system?

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Recommendation Engines

Q50. What are different methods to measure degree of similarity?

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Recommendation Engines

Q51. How should one choose the appropriate similarity metric for candidate generation in recommendation systems?

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Recommendation Engines

Q52. How can we utilize content-based filtering in the candidate generation process?

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Recommendation Engines

Q53. State the advantages and disadvantages of content-based filtering?

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Recommendation Engines

Q54. What is the cold start problem in recommendation systems, and how can you address it?

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Recommendation Engines

Q55. State explicit and implicit feedback with examples?

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Recommendation Engines

Q56. How does collaborative filtering work?

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Recommendation Engines

Q57. What are the advantages and disadvantages of collaborative filtering?

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Recommendation Engines

Q58. How can user and item embeddings be learned using matrix factorization in collaborative filtering?

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Recommendation Engines

Q59. What is the issue with learning embeddings using matrix factorization?

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Recommendation Engines

Q60. How can we use Deep Neural Network model for recommendation?

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Recommendation Engines

Q61. What is the benefit of using matrix factorization to learn embeddings over softmax DNN?

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Recommendation Engines

Q62. What is the benefit of using softmax DNN over matrix factorization for learning embeddings?

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Recommendation Engines

Q63. What strategies can be employed to efficiently compute the nearest neighbors in the embedding space of a recommendation system, and how do they work?

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Recommendation Engines

Q64. Why should we avoid using candidate generators to rank items in a recommendation system, and what are the benefits of separating candidate generation from the ranking process?

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Recommendation Engines

Q65. How does the choice of scoring function affects the ranking of items and quality of recommendations?

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Recommendation Engines

Q66. What is the benefit of re-ranking in recommendation system?

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Recommendation Engines

Q67. Imagine we build a user-item collaborative filtering system to recommend to each user items similar to the items they’ve bought before.

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You can build either a user-item matrix or an item-item matrix. What are the pros and cons of each approach?
How would you handle a new user who hasn’t made any purchases in the past?

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