This was an applied scientist internship for (explicitly) Computer Vision, Machine Learning, Speech Technologies, and Robotics. I state the position I applied for as it becomes more relevant later in the process. My recruiter first messaged me stating that someone from the ‘Vesta’ team was wanting to interview me. I did not know about the ‘Vesta’ team, so I tried googling. I found very little, other than articles from 2018/2019 about a home robot. Since I was trying to prepare for this interview, I asked the recruiter what I can expect during the interviews and what topics I may be asked. I was told the first interview was on ‘Domain Expertise’ and the second was ‘Problem Solving’. The recruiter said that the interviewers generate their own questions for the interviews. I may be asked ‘machine learning-related’ questions during the ‘Domain Expertise’ as well as the ‘Problem Solving’ interview. The recruiter also said there may be small coding questions in either interview. I personally have fair amount experience in Machine Learning and Computer Vision, so I was not terribly concerned by this. The first interview went in my opinion, really well. I was asked machine learning and behavioral questions based on my previous experience. The second interview should have its named changed from ‘Problem Solving’ to “Reinforcement Learning Expertise”. First of all, I do not have a lot of experience in Reinforcement Learning. I have one project on my resume which is based on reinforcement learning and the remaining five are Computer Vision and Machine Learning related. Second, I applied for “Applied Science Intern - Speech Technologies, Computer Vision, Machine Learning, Robotics”. You could easily say the Robotics part covers Reinforcement Learning, but I was not expecting to be chosen for that. The limited information I received from the recruiter explicitly stated, ‘Machine Learning’, and it was mentioned multiple times. Needless to say, I was not prepared for a fully ‘Reinforcement Learning’ interview. I feel mislead by my recruiter because I was provided very limited information about the internship. Literally, what I wrote above, is all I was given…And the recruiter told me multiple times it was a machine learning internship. - ( I recorded my responses, and I am deriving the questions from my response ) - Now that I wrote out my questions… I feel like I was asked a lot of questions… Interview 1 Questions: • What is Bagging and how is it implemented? • What is an example of a Bagging algorithm? • What is Boosting and how is it implemented? • What is an example of a Boosting algorithm? • What is the difference between Bagging and Boosting? • Have you heard of a Decision Tree? • What are some ways to split the data at the nodes in the tree? • What are some disadvantages of Decision Trees? • What is Overfitting? • What are some ways to reducing a Decision Trees ability to overfit? • What is Bias and Variance? • What is the Bias-Variance Trade off? • What is an Ensemble and how is it implemented? • What is an example of an Ensemble? • Given a Balanced classification data set, what are ways you could measure the performance of your model? • What is Precision? • What is Recall? • What is F1-Score? • What if the data set is unbalanced, what are some measurements you could use? • What is ROC curve? • How do you interpret the ROC curve? • What is AOC? • What are some ways to reduce overfitting? • What is Regularization? • What is the difference between L1 and L2? • What are some ways of reducing the complexity of a model? • Specifically, WRT Deep Learning, what are some techniques of regularization? • How does an Autoencoder work? • What is the Curse of Dimensionality? • Ways to counteract the Curse of Dimensionality? • What is PCA and ICA? • How could I use a Random Forest for Dimensionality Reduction? Interview 2 Questions: • Tell me about yourself • What is a Random Forest (asked in both interviews)? • How does a Random Forest work? • What is a reason for using a Random Forest? • Methods of splitting on the nodes? • What are some ways to reduce overfitting in Random Forest with unlimited depth trees? • If I wanted to convert a pruned decision tree to a regression tree, how would I do it? • What is Feature Importance, WRT decision trees? • What are some dimensionality reduction techniques? • What is a MDP (Markov Decision Process)? • What is a Markov Chain? • What is the difference between an MDP and Markov Chain? • What is a Transition Matrix? • How do you get a Transition Matrix? • What is the formula for the Bellman Equation? • What does the Bellman Equation do? • What is a Policy? • What are ways to get/make/create a Policy? • What is Value-Iteration? • What is Policy-Iteration? • What is Q-Learning? • Does Q-Learning use Policy-Iteration or Value-Iteration? • What is a Q-Table? • How is a Q-Table populated? ... Not Enough room, adding remaining as follow-up