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Free Problems
Chapter 05 - Agent Orchestration
This problem set covers key concepts from Chapter 5 on Agent Orchestration, including different agent types, tool selection strategies, execution patterns, and context engineering. These problems will test your understanding of how to effectively orchestrate AI agents to solve complex tasks using various planning and execution strategies.
27 pts Medium 103 agent types agent orchestration planning and execution +7
Chapter 4 - Tool Use in AI Agents
This problem set covers the key concepts from Chapter 4 on Tool Use in AI Agents. You'll explore different types of tools (local, API-based, plug-in, MCP), learn how to implement them using LangChain, and understand the security considerations and best practices for tool development and deployment. The problems progress from basic concepts to advanced implementation scenarios.
29 pts Medium 104 ai agents tool use ai definitions +7
Chapter 03 - User Experience Design for Agentic Systems
This problem set covers key concepts from Chapter 3 on User Experience Design for Agentic Systems. The chapter explores interaction modalities, autonomy sliders, context retention, trust building, and design principles for creating effective agent experiences. These problems test your understanding of how to design intuitive, trustworthy, and effective agent systems across different modalities and interaction patterns.
29 pts Medium 101 user experience design agentic systems interaction modality +7
Chapter 02 - Designing Agent Systems
This problem set covers key concepts from Chapter 2: Designing Agent Systems. You'll be tested on agent system components, design trade-offs, architecture patterns, and best practices for building effective agent-based systems. The problems progress from basic concepts to advanced design considerations.
26 pts Medium 101 agent systems core components system design +7
Chapter 01 - Introduction to Agents
This problem set covers key concepts from Chapter 1: Introduction to Agents, focusing on autonomous agents, their characteristics, types, model selection, and practical applications. The problems progress from basic definitions to advanced analytical questions about agent design and implementation.
19 pts Easy 100 autonomous agents agent systems artificial intelligence +7
Chapter 11 - Agent Planning and Feedback
This problem set covers key concepts from Chapter 11 on agent planning and feedback. You'll explore sequential vs. parallel planning, feedback mechanisms, custom planner implementation, and applications of planning, reasoning, evaluation, and feedback in agentic systems. The problems progress from basic concepts to advanced implementation scenarios.
25 pts Easy 96 agent planning chatbot vs agent feedback loops +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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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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