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Could you discuss the computational complexity involved in training an SVM? What is the time complexity associated with making predictions using a trained SVM? How does the choice of kernel in an SVM affect the time complexity for both training and inference?

Take me through your thought process on leveraging a hash table to solve the two sum problem with a O(n) time complexity.

How would you explain the concept of log loss to a peer?

Describe any difficulties encountered while working on supervised machine learning models and suggest remedies.

Can you explain your thought process for determining the appropriate value for "k" when implementing the K-means algorithm?

What are the advantages and disadvantages of decision trees when compared to other classification algorithms?

What strategies would you employ to develop a model that effectively recognizes objects in a frame?

Let's assume we can only use a random number generator. What steps would you take to generate i.i.d. draws from distribution X?

Write a function that can identify the subarray of length k with the maximum sum from a given array of integers.