What is your weakness?
Research Trainee Interview Questions
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Introduction of previous research.
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.
a lot of math problems, statistics, machine learning, functional analysis
What is the convolutional layer in neural networks and how does it work?
Given a decimal number, compute the reverse.
What do you think the future of this field of science is?
Potential approaches to a partial differential equation
Do you have previous lab experience?
Personal question about marital status.
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