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Free Problems
Chapter 06 - Ensemble Learning and Random Forests
This problem set covers key concepts from Chapter 6 on Ensemble Learning and Random Forests. You'll explore voting classifiers, bagging, random forests, boosting methods, and stacking ensembles. These problems test your understanding of how combining multiple models can create more powerful predictors than any single model alone.
28 pts Medium 99 ensemble learning random forests machine learning +7
Chapter 05 - Decision Trees
This problem set covers key concepts from Chapter 5 on Decision Trees, including tree structure, prediction mechanics, Gini impurity, CART algorithm, regularization, and practical applications. Work through these problems to test your understanding of decision tree fundamentals and their implementation in machine learning.
26 pts Medium 98 decision trees iris dataset machine learning +7
Chapter 3 - Classification Fundamentals
This problem set covers key concepts from Chapter 3 on Classification, including binary classification, performance metrics, confusion matrices, precision/recall trade-offs, ROC curves, and multiclass classification strategies. Work through these problems to test your understanding of classification fundamentals using the MNIST dataset as discussed in the chapter.
24 pts Easy 104 binary classification mnist dataset target vectors +7
Chapter 02 - End-to-End Machine Learning Project
This problem set covers the complete machine learning project workflow from Chapter 2, including data exploration, preprocessing, model selection, evaluation, and deployment. Practice these essential concepts through a variety of question types that test your understanding of the end-to-end ML process.
24 pts Easy 101 machine learning regression data analysis +7
Chapter 01 - The Machine Learning Landscape
This problem set covers fundamental concepts from Chapter 1 of Hands-On Machine Learning. Test your understanding of machine learning definitions, types of learning systems, challenges, and evaluation methods. These questions progress from basic concepts to more advanced analytical thinking about ML systems.
26 pts Medium 95 machine learning tom mitchell learning components +7
Chapter 05 - Collective Memory and Organizational Knowledge Sharing
This problem set explores the concepts of collective memory and organizational knowledge sharing through AI agents, based on Chapter 5 of the O'Reilly book "Managing Memory for AI Agents." The problems cover transactive memory systems, AI-powered knowledge platforms, memory preservation strategies, and human-AI collaboration patterns. These questions progress from foundational concepts to advanced applications of organizational memory systems.
14 pts Medium 99 transactive memory system organizational knowledge sharing collective memory +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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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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