Topics: Machine Learning, Neural Networks, Bootstrap Sampling, Decision Trees
Location: San Francisco Bay Area
Interview date: 2025-09-01
Summary
The online assessment consisted of six multiple-choice questions on machine learning, one question on calculating a neural network with sigmoid, a question on manually implementing bootstrap sampling, a LeetCode problem involving expression minimization, and a question on manually creating a decision tree.
Details
The technical assessment included a variety of machine learning fundamentals and coding challenges.
ML Multiple Choice: Six questions testing core machine learning knowledge.
Neural Network Calculation: Required manual calculation of a neural network, including the sigmoid function. A calculator was necessary.
Bootstrap Sampling: Involved implementing bootstrap sampling from scratch.
Expression Minimization (LeetCode-style): Given an expression like '1639+5628', I had to add parentheses to minimize the result. For example, transforming it into '16(39+5)628'. The parentheses had to be added to either side of the '+'. The goal was to traverse all possibilities to find the minimum arithmetic result.
Decision Tree Implementation: Manually implementing a decision tree.
Preparation Tips & Key Takeaways
What I Learned
Brush up on fundamental machine learning concepts for multiple-choice questions.
Ensure you can perform manual calculations related to neural networks.
Practice implementing machine learning algorithms from scratch, such as bootstrap sampling and decision trees.
Sharpen your algorithmic problem-solving skills, especially for expression manipulation problems.
Recommended Preparation
Machine Learning
Review key concepts like bias-variance tradeoff, regularization, and common algorithms.
Coding Practice
Practice tree-based algorithms and problems that require string/expression manipulation.
Math
Brush up on math concepts used in machine learning
Resources I Recommend
Standard machine learning textbooks and online courses.