A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

A collection of research papers on decision, classification and regression trees with implementations.

《统计学习方法》笔记-基于Python算法实现

The Elements of Statistical Learning (ESL)的中文翻译、代码实现及其习题解答。

Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

Teaching Materials for Dr. Waleed A. Yousef

A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book

Jupyter notebooks for summarizing and reproducing the textbook "The Elements of Statistical Learning" 2/E by Hastie, Tibshirani, and Friedman

My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman

Lecture Slides and R Sessions for Trevor Hastie and Rob Tibshinari's "Statistical Learning" Stanford course

统计学习方法训练营课程作业及答案，视频笔记在线阅读地址：https://relph1119.github.io/statistical-learning-method-camp

TAPAS - Translational Algorithms for Psychiatry-Advancing Science

An Introduction to Statistical Learning with Applications in PYTHON

📖 List of some awesome university courses for Machine Learning! Feel free to contribute!

Generalized Linear Models in Sklearn Style

Advanced Normalization Tools in R

Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.

Introduction to Statistical Learning with R을 Python으로

A list of classic books make better you understand not only how it works, but why it works.

D-Lab's Machine Learning Working Group at UC Berkeley, with supervised & unsupervised learning tutorials in R and Python

Learn Deep Learning The Hard Way

李航《统计学习方法》笔记和 Python 实现（不基于任何代数运算库）。

📈 Machine Learning from the perspective of a Statistician using R

Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics

SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.

Statistical Models with Regularization in Pure Julia