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Free Problems
Dynamic Asset Allocation Concepts
This problem set covers key concepts from Chapter 8 on Dynamic Asset Allocation, including two-fund separation, 60/40 portfolios, capital market line, and reinforcement learning applications in portfolio management. The problems test understanding of theoretical foundations, practical implementations, and analytical reasoning related to dynamic asset allocation strategies.
27 pts Medium 100 capital market line asset pricing financial mathematics +7
Dynamic Hedging and Option Replication
This problem set covers key concepts from Chapter 7 on Dynamic Hedging, focusing on the Black-Scholes-Merton (1973) model, delta hedging, option replication, and reinforcement learning applications in finance. The problems test understanding of geometric Brownian motion, delta calculation, discrete-time replication, and the HedgingAgent implementation.
39 pts Medium 95 stochastic differential equations black-scholes-merton model risk-neutral pricing +7
Algorithmic Trading with Deep Q-Learning
This problem set explores the application of Deep Q-Learning to algorithmic trading, based on Chapter 6 of the O'Reilly book "Reinforcement Learning for Finance". The chapter demonstrates how to transform a financial prediction game into an algorithmic trading system using simulated financial time series data and multiple financial features. These problems will test your understanding of the Trading environment, TradingAgent class, financial feature engineering, and performance evaluation in algorithmic trading contexts.
26 pts Medium 99 deep q-learning algorithmic trading reinforcement learning +7
Chapter 05 - Generated Data with GANs
This problem set covers Generative Adversarial Networks (GANs) as introduced in Chapter 5. You'll explore the fundamental concepts of GAN architecture, training dynamics, and practical applications in generating synthetic financial data. The problems progress from basic conceptual understanding to advanced analytical thinking about GAN performance evaluation.
27 pts Medium 95 generative adversarial networks deep learning machine learning +7
Chapter 04 - Simulated Data for Reinforcement Learning
This problem set covers key concepts from Chapter 4: Simulated Data, focusing on data augmentation techniques for training deep Q-learning agents in financial applications. You'll explore adding noise to historical data and simulating financial time series using stochastic processes like the Vasicek model. These techniques address the limitation of relying on single historical time series by generating unlimited training data.
27 pts Medium 97 reinforcement learning data augmentation deep q-learning +7
Chapter 3 - Financial Q-Learning
This problem set covers key concepts from Chapter 3: Financial Q-Learning, focusing on the implementation of a Finance environment for reinforcement learning, the DQL agent architecture, and the limitations of applying gaming environments to financial problems. The problems progress from basic understanding to advanced analytical thinking about the chapter's core concepts.
39 pts Medium 100 reinforcement learning q-learning environment design +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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Cover of System Design Interview: An Insider's Guide Volume 2
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Cover of UNICALLI: A UNIFIED DIFFUSION FRAMEWORK FOR COLUMN-LEVEL GENERATION AND RECOGNITION OF CHINESE CALLIGRAPHY
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Featured Books

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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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Cover of Flow-Matching vs Diffusion Models explained side by side
Flow-Matching vs Diffusion Models explained side by side
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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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