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Free Problems
Chapter 18 - Numeric Python
This problem set covers data input and output concepts from Chapter 18, focusing on various file formats for storing numerical data including CSV, HDF5, Parquet, JSON, and serialization methods. The problems test understanding of when to use each format, their advantages and limitations, and practical implementation using Python libraries.
28 pts Medium 95 csv file-formats data-storage +7
Chapter 17 - Numeric Python
This problem set covers key concepts from Chapter 17 on Signal Processing, including spectral analysis, Fourier transforms, window functions, spectrograms, and signal filters. The problems progress from fundamental concepts to practical applications using SciPy's signal processing capabilities.
30 pts Medium 100 sampling-theorem nyquist-frequency signal-processing +7
Chapter 16 - Numeric Python
This problem set covers Bayesian Statistics concepts from Chapter 16, including Bayesian inference, PyMC model specification, MCMC sampling, posterior distributions, and Bayesian linear regression. The problems progress from fundamental concepts to practical implementation using PyMC.
29 pts Medium 96 bayesian-statistics frequentist-statistics statistical-inference +7
Chapter 15 - Numeric Python
This problem set covers key concepts from Chapter 15 on Machine Learning, focusing on supervised vs. unsupervised learning, regularization techniques, classification methods, clustering algorithms, and evaluation metrics. The problems progress from fundamental concepts to practical implementation using scikit-learn.
27 pts Easy 101 machine-learning supervised-learning unsupervised-learning +7
Chapter 14 - Numeric Python
This problem set covers key concepts from Chapter 14: Statistical Modeling, focusing on statistical modeling with statsmodels and Patsy, linear regression, discrete regression, and time series analysis. The problems test understanding of model specification, interpretation of results, and practical application of statistical modeling techniques.
30 pts Medium 104 patsy formula-syntax statistical-modeling +7
Chapter 13 - Numeric Python
This problem set covers key concepts from Chapter 13: Statistics, focusing on descriptive statistics, random number generation, probability distributions, hypothesis testing, and nonparametric methods using NumPy and SciPy's stats module. The problems progress from basic to advanced difficulty and test both conceptual understanding and practical implementation skills.
30 pts Medium 95 variance degrees-of-freedom descriptive-statistics +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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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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