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Can you name a few optimization algorithms you are familiar with? What's your approach to managing a high-dimensional space with a million variables in optimization? Can you explain the simplex method used in optimization problems?

How does information theory serve in the comparison of language models? Provide an instance of how you might apply this in practice.

Can you demonstrate how to create a normal distribution of 1000 data points with a mean of x and variance of y in R, perform a statistical summary, and plot the density? Follow this by executing the same in Python using the Codility platform.

Dimension reduction is an essential aspect of Machine Learning. Could you list some methods used to achieve this and their specific use cases?

In Python, how do you script out the implementation of a hash table?

Would you be able to devise a Python class that provides iteration capabilities over sequences?

How would you differentiate between precision and recall in the field of data analysis? Can you think of any scenarios where one of these metrics may be more relevant than the other?

Build a payment scheduling system for Anaplan

Please explain the steps you take to turn raw data into something that a machine learning algorithm can interpret.

Which kinds of problems are better suited for neural networks than support vector machines? What distinguishes neural networks and SVMs in their methodologies?