Table of Contents

Probability

Probability and randomization are foundational concepts in AI and machine learning, enabling models to handle uncertainty and make predictions under incomplete information. This blog explores coding questions that cover these essential topics, starting with basic concepts and progressing to advanced applications. Whether you're preparing for AI/ML interviews or seeking a deeper understanding of these principles, this guide will equip you with practical problem-solving techniques and insights into real-world applications.

Core Knowledge

Core Probability Concepts

Randomization Techniques

Sampling and Simulation

Advanced Topics

Key Questions

StatusQuestionCategory
Generate Random Number Between Two Integers
Probability
Implement rand0N Using rand02
Probability
Implement Fair Coin from a Biased Coin
Probability
Weighted Random Selection
Probability
Efficient Random Shuffling of a List
Probability
Monte Carlo Circle Area Estimation
Probability
Importance Sampling for Mean Estimation of a Skewed Distribution
Probability
Random Walk Simulation
Probability
Gaussian Random Number Generator
Probability
Sampling for Stream Data
Probability

Common Pitfalls

Extended Questions

StatusQuestionCategory
Memory-Efficient Weighted Sampling for Streaming Data with Dynamic Weights
Large-Scale Random Operations
Distributed Unique Random ID Generator
Large-Scale Random Operations
Implement Probabilistic Data Structures for Big Data
Large-Scale Random Operations
Implement Alias Method for O(1) Weighted Random Selection
Large-Scale Random Operations
Multithreaded Random Walk Simulation with Collision Detection
Parallel Randomization
Batch Generation of Poisson-Distributed Numbers with Vectorization
Parallel Randomization
Implement Thompson sampling for multi-armed bandit problem
Advanced Probability Coding
Bayesian A/B testing simulation with posterior probability updates
Advanced Probability Coding
Generate Correlated Normal Random Variables
Advanced Probability Coding

Applications

  • Neural network parameter initialization
  • Efficient training data sampling techniques
  • Training machine learning models with stochastic gradient descent (SGD)
  • Data augmentation strategies for computer vision and NLP
  • Simulation of natural phenomena in reinforcement learning
  • Enhancing recommendation diversity in recommender systems
  • Building probabilistic data structures like Bloom filters for membership testing
  • Implementing locality-sensitive hashing (LSH) for nearest neighbor search