First part of the interview was kind of rapid fire of questions on machine learning basics: 01. Given a list of ML algorithms, briefly explain which ones belong to supervised and which ones belong to unsupervised. 02. Definition of precision, recall, and f1-score 03. How to synthesize data using KNN algorithm 04. How to do classification using Decision Tree 05. How to do normalization during data pre-processing 06. What is overfitting, what could cause it, and what would be the suggested solutions 07. What are MSE, OLS, MAP, MLP? 08. Does DNN suffer from curse of dimensionality? Why or why not? There should be about 10-12 questions, the rest of which I have forgotten.
