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Free Problems
Chapter 12 - Protecting Agentic Systems
This problem set covers key security concepts from Chapter 12 on protecting agentic systems. You'll explore unique risks of autonomous agents, emerging threat vectors, foundation model security, data protection strategies, and agent safeguards. These problems test your understanding of how to secure AI agents against both external threats and internal failures in complex, dynamic environments.
28 pts Medium 98 agentic systems ai alignment autonomous agents +7
Chapter 11 - Improvement Loops
This problem set covers key concepts from Chapter 11: Improvement Loops, focusing on feedback pipelines, experimentation frameworks, and continuous learning mechanisms for multiagent systems. The problems progress from foundational concepts to advanced applications, testing your understanding of how agent systems can adapt and improve over time through systematic feedback-driven processes.
31 pts Medium 100 multiagent systems continuous improvement system cycles +7
Chapter 10 - Monitoring in Production
This problem set covers key concepts from Chapter 10 on monitoring agent-based systems in production. You'll be tested on monitoring stacks, instrumentation techniques, visualization, alerting strategies, and monitoring patterns for probabilistic AI systems. The problems progress from foundational concepts to advanced implementation details.
27 pts Medium 95 agent-based systems system monitoring production systems +7
Chapter 09 - Validation and Measurement
This problem set covers key concepts from Chapter 9 on Validation and Measurement for agent-based systems. You'll explore measurement strategies, evaluation techniques, component testing, and deployment readiness for AI agents. The problems progress from fundamental concepts to advanced implementation scenarios, testing your understanding of rigorous evaluation methodologies for modern agentic systems.
26 pts Medium 100 agent-based systems validation measurement +7
Multi-Agent Systems Design and Coordination
This problem set covers key concepts from Chapter 8: "From One Agent to Many" focusing on multi-agent system design, coordination strategies, communication techniques, and the principles for scaling from single-agent to multi-agent architectures. The problems test understanding of when and how to use different agent coordination patterns, communication frameworks, and design principles for building effective multi-agent systems.
29 pts Medium 97 single-agent systems multi-agent systems system design +7
Learning in Agentic Systems
This problem set covers key concepts from Chapter 7 on learning in agentic systems, including nonparametric learning techniques (exemplar learning, Reflexion, experiential learning) and parametric learning approaches (supervised fine-tuning, direct preference optimization). The problems progress from basic concepts to advanced implementation details, testing your understanding of how agents can learn and improve over time through interaction with their environment.
30 pts Medium 101 machine learning parametric learning nonparametric 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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Hands-On Machine Learning with Scikit-Learn and PyTorch
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Deep Reinforcement Learning Hands-On - Third Edition
222 questions 720 pts

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Attention in transformers, step-by-step | Deep Learning Chapter 6
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Cover of Knowledge Distillation: How LLMs train each other
Knowledge Distillation: How LLMs train each other
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Diffusion Model
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Popular Topics

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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