There are 29 repositories under statistical-learning topic.
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.
The Elements of Statistical Learning (ESL)的中文翻译、代码实现及其习题解答。
This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement 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 Springer book "Python for Probability, Statistics, and Machine Learning"
Machine Learning library for the web and Node.
My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman
Jupyter notebooks for summarizing and reproducing the textbook "The Elements of Statistical Learning" 2/E by Hastie, Tibshirani, and Friedman
SmartCore is a comprehensive library for machine learning and numerical computing. The library provides a set of tools for linear algebra, numerical computing, optimization, and enables a generic, powerful yet still efficient approach to machine learning.
Lecture Slides and R Sessions for Trevor Hastie and Rob Tibshinari's "Statistical Learning" Stanford course
An Introduction to Statistical Learning with Applications in PYTHON
Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels
TAPAS - Translational Algorithms for Psychiatry-Advancing Science
Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
JuliaLang version of "An Introduction to Statistical Learning: With Applications in R"
A list of time-lasting classic books, which could not only help you figure out how it works, but also grasp when it works and why it works in that way.
📖超详细《统计学习方法：李航》笔记 200 页，包含了很多详细的公式推导和案例实践，已经整理成pdf，有详细的目录。
Generalized Linear Models in Sklearn Style
:book: List of some awesome university courses for Machine Learning! Feel free to contribute!
My book list
Advanced Normalization Tools in R
Introduction to Statistical Learning with R을 Python으로
Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics
:chart_with_upwards_trend: Machine Learning from the perspective of a Statistician using R
D-Lab's Machine Learning Working Group at UC Berkeley, with supervised & unsupervised learning tutorials in R and Python
李航《统计学习方法》笔记和 Python 实现（不基于任何代数运算库）。
Solutions to exercises from Introduction to Statistical Learning (ISLR 7th Edition)
Matlab library for gradient descent algorithms: Version 1.0.1