Ресурсы
Курсы, книги и лекции для самостоятельного изучения Математики, Computer Science, Экономики, Machine Learning и Deep Learning
Темы
Математика
Доказательства и дискретная математика
Линейная алгебра
Boyd/Vandenberghe applied linear algebra ↗
Brunton SVD/PCA and computational examples ↗
CMU Linear Algebra Review — J. Zico Kolter ↗
MIT A Vision of Linear Algebra — Gilbert Strang ↗
Linear Algebra: Foundations to Frontiers — LAFF ↗
Advanced Linear Algebra: Foundations to Frontiers — ALAFF ↗
Linear Algebra Done Right, 4th ed. — Axler ↗
Immersive Linear Algebra — Ström, Åström & Akenine-Möller ↗
Interactive Linear Algebra — Margalit & Rabinoff, Georgia Tech ↗
Calculus и анализ
MIT 18.01SC Single Variable Calculus ↗
MIT 18.02SC Multivariable Calculus ↗
MIT 18.125 Measure and Integration ↗
MIT 18.03 Differential Equations — Spring 2010 ↗
Вероятность и статистика
Brunton Probability Bootcamp ↗
Introduction to Statistics and Data Analysis — Steve Brunton ↗
NIST experimental-design handbook ↗
Seeing Theory — Brown University ↗
Aalto Bayesian Data Analysis — Aki Vehtari ↗
Statistical Rethinking 2026 — Beginner track ↗
Introduction to Probability, Statistics, and Random Processes — Pishro-Nik ↗
Оптимизация и численные методы
Brunton Optimization Bootcamp videos ↗
MIT 18.330 Numerical Analysis — OCW 2012 ↗
Princeton ELE539/COS512 Optimization for Machine Learning — Chi Jin, Spring 2021 ↗
Mathematics for Machine Learning — Deisenroth et al. ↗
Convex Optimization — Boyd & Vandenberghe ↗
Fundamentals of Numerical Computation — Driscoll & Braun ↗
UW AMATH 301 Beginning Scientific Computing — Brunton/Kutz materials ↗
Computer Science
Алгоритмы и структуры данных
Distributed Systems
Hivemind — decentralized deep learning in PyTorch ↗
DiLoCo: Distributed Low-Communication Training of Language Models ↗
Aalto CS-E4740 Federated Learning ↗
Компиляторы и ML Compilation
Machine Learning Compilation course ↗
Cornell CS6120 Advanced Compilers — 2025 self-guided course ↗
Cornell CS4120 Introduction to Compilers — Spring 2026 ↗
Machine Learning
Introduction to Statistical Learning with Applications in Python — James et al. ↗
Introduction to Machine Learning — Sebastian Raschka ↗
Yandex Machine Learning Handbook ↗
Introduction to Machine Learning — Konstantin Vorontsov ↗
Probabilistic Machine Learning: An Introduction — Murphy ↗
Understanding Machine Learning — Shalev-Shwartz & Ben-David ↗
CMU 10-708 Probabilistic Graphical Models — Spring 2020 lecture index ↗
Данные, forecasting и evaluation
Predictive uncertainty and model reliability — calibration guide ↗
Optuna optimization tutorial ↗
MIT Introduction to Data-Centric AI — IAP 2024 ↗
DuckDB guides — reproducible ML data engineering ↗
Forecasting: Principles and Practice, the Pythonic Way ↗
Applied Causal Inference Powered by ML and AI ↗
Deep Learning и PyTorch
Understanding Deep Learning — Prince ↗
Deep Learning: Foundations and Concepts — Bishop & Bishop ↗
YSDA Practical Deep Learning ↗
NLP, LLMs и agents
LLMs from Scratch — бесплатные видео и код ↗
Yandex School of Data Analysis NLP course ↗
Introduction to Information Retrieval — Manning, Raghavan & Schütze ↗
Speech and Language Processing, 3rd-edition draft — Jurafsky & Martin ↗
Reinforcement Learning и post-training
Lambert post-training course ↗
YSDA Practical Reinforcement Learning ↗
CMU 17-740 Algorithmic Foundations of Interactive Learning — Spring 2025 ↗
Reinforcement Learning: An Introduction, 2nd ed. — Sutton & Barto ↗
Generative Models и Computer Vision
Stanford CS236 public 2023 recordings ↗
MIT 6.S184 Flow Matching and Diffusion — 2026 ↗
Computer Vision: Algorithms and Applications, 2nd ed. — Szeliski ↗
Parallel Computing и GPU kernels
ML Systems
CMU Machine Learning Systems ↗
MLSysBook labs / performance modeling ↗
UCSD CSE234 Data Systems for Machine Learning — Winter 2025 ↗
LLM Inference
Baseten's Inference Engineering ↗
Tiny-LLM — Build an LLM Serving System with MLX ↗
Serving и distributed inference
Исследовательская практика
Экономика
Микроэкономика и макроэкономика
MIT 14.01 Microeconomics — Fall 2023 ↗
Равновесие и game theory
Эконометрика и causal inference
MIT 14.310x Data Analysis for Social Scientists ↗
MIT 14.32 Econometrics — Spring 2007 ↗
Opportunity Insights — Using Big Data to Solve Economic and Social Problems ↗
Труд и экономика AI
MIT 14.64 Labor Economics and Public Policy — reading guide ↗
Acemoglu & Restrepo — Artificial Intelligence, Automation and Work ↗
The Race Between Machine and Man ↗
Computational Economics
Garin, Lester & Sims — Intermediate Macroeconomics ↗
Политика и благосостояние
MIT 14.41 Public Finance — Fall 2024 ↗
Korinek & Juelfs — Preparing for the (Non-existent?) Future of Work ↗
Acemoglu — The Simple Macroeconomics of AI ↗
Athey & Scott Morton — Artificial Intelligence, Competition, and Welfare ↗
Brynjolfsson, Korinek & Agrawal — A Research Agenda for the Economics of Transformative AI ↗
Finance, development и другие направления
MIT 18.642 Mathematics with Applications in Finance — Fall 2024 ↗
MIT 14.73 The Challenge of World Poverty — Spring 2011 ↗
MIT 14.771 Development Economics — Fall 2021 ↗
MIT 14.13 Psychology and Economics — Spring 2020 ↗
Fernando Borretti — No-One Escapes the Permanent Underclass ↗