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Free Problems
Chapter 22 - Multi-Agent Reinforcement Learning Concepts
This problem set covers key concepts from Chapter 22 on Multi-Agent Reinforcement Learning (MARL), including environment setup, agent interactions, observation spaces, and training approaches. The problems progress from basic MARL concepts to advanced implementation details and analytical reasoning about multi-agent systems.
38 pts Medium 102 reinforcement learning multi-agent systems agent interaction +7
Chapter 21 - RL in Discrete Optimization - Rubik's Cube Applications
This problem set explores reinforcement learning applications in discrete optimization, specifically focusing on the Rubik's cube puzzle. Based on the autodidactic iteration (ADI) method from McAleer et al., these problems test understanding of state representations, neural network architectures, training processes, and Monte Carlo Tree Search (MCTS) for solving combinatorial optimization problems.
49 pts Medium 94 group theory combinatorics rubiks cube +7
Chapter 20 - AlphaGo Zero and MuZero Concepts
This problem set covers key concepts from Chapter 20 on AlphaGo Zero and MuZero model-based reinforcement learning methods. These methods revolutionized board game AI by enabling agents to improve through self-play without human knowledge. The problems test understanding of MCTS, neural network architectures, training processes, and the differences between model-based and model-free approaches.
35 pts Medium 96 reinforcement learning model-based learning model-free learning +7
Chapter 19 - Reinforcement Learning with Human Feedback
This problem set covers key concepts from Chapter 19 on Reinforcement Learning with Human Feedback (RLHF). You'll explore the motivation behind RLHF, its theoretical foundations, implementation details, and practical applications in both traditional RL environments and modern LLM training pipelines. The problems progress from basic conceptual understanding to advanced analytical thinking about RLHF systems.
33 pts Medium 98 reinforcement learning human feedback machine learning +7
Chapter 10 - Stocks Trading Using RL
This problem set covers key concepts from Chapter 10: Stocks Trading Using RL, which demonstrates how to apply reinforcement learning to financial trading. The problems test your understanding of the trading environment design, data representation, reward systems, and model architectures used in this practical RL application.
22 pts Medium 101 reinforcement learning rl components stock trading +7
Chapter 01 - Deep Reinforcement Learning Hards-On Chapter 1
This problem set covers the fundamental concepts of reinforcement learning from Chapter 1, including the differences between RL and other ML paradigms, Markov processes, reward systems, and the core components of RL systems. These questions test your understanding of the theoretical foundations that underpin modern reinforcement learning approaches.
13 pts Medium 102 reinforcement learning supervised learning unsupervised learning +7
Premium Problems
Python I/O and Data Pipeline Assessment - Part 4
20 questions focused on PyTorch Dataset/DataLoader design: map/iterable datasets, transforms, custom collate/padding, worker seeding/sharding, num_workers/pin_memory/prefetch_factor, caching, memmap/shared memory, batching by size, profiling, and performance tuning.
10.00 60 pts Medium 98 torch.utils.data.dataset pytorch dataset +7
Chapter 02 - Numeric Python
This problem set covers key concepts from Chapter 2: Vectors, Matrices, and Multidimensional Arrays. The problems test understanding of NumPy array fundamentals, including array creation, indexing, slicing, operations, and vectorized computing. Each question is designed to reinforce the core concepts presented in the chapter.
5.00 26 pts Medium 97 numpy-arrays array-attributes shape +7
USAAIO 2025 R1P3 - Logistic Regression Implementation
This problem focuses on implementing logistic regression from scratch using the Titanic dataset. You will work through data pre-processing, mathematical derivations, and implement both gradient descent and Newton's method for logistic regression. The dataset contains passenger information from the Titanic, and your goal is to predict survival based on various features.
10.00 48 pts Easy 93 data-loading pandas data-exploration +7
USAAIO 2025 R1P2 - Basics of Neural Network - From Linear Regression to DNN Training
This problem is about the basics of neural network. Each part has its particular purpose to intentionally test you something. Do not attempt to find a shortcut to circumvent the rule. And all coding tasks shall run on CPUs, **not GPUs**.
10.00 36 pts Easy 96 learning-rate-scheduler pytorch optimization +12
USAAIO 2025 R1P1 - Fibonacci Matrix Form
Let us consider the following sequence: $$ F_n = F_{n-1} + F_{n-2},\ \forall\ n \ge 2. $$
8.00 27 pts Medium 96 fibonacci sequence linear algebra matrix form +7
IAIO 2024 Part 2 - Machine Learning Algorithms and Deep Learning
This problem covers the remaining categories of the 2024 International Artificial Intelligence Olympiad (IAIO), focusing on machine learning algorithms and deep learning. You'll work through practical implementations of k-means clustering, deep learning architectures, and advanced machine learning theory including kernel methods and the Perceptron algorithm. The problems cover: - K-means clustering algorithm implementation and convergence - Deep learning architectures (DALL-E, Transformers) - Perceptron algorithm and kernel methods - Mathematical proofs and theoretical analysis - Parameter counting and computational complexity
10.00 44 pts Hard 99 k-means clustering euclidean distance machine learning +7

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Featured PDFs

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Attention in transformers, step-by-step | Deep Learning Chapter 6
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Knowledge Distillation: How LLMs train each other
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Diffusion Model
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machine learning 56 deep learning 40 neural networks 35 reinforcement learning 33 system-design 28 grade5 27 optimization 14 large language models 13 attention mechanisms 13 combinatorics 13 system-architecture 13 natural language processing 12 aime problems 12 Number Sense 12 scalability 11 beginner 10 number theory 10 performance 10 transformers 9 capacity-planning 9
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