ajtulloch / Elements-of-Statistical-Learning
Contains LaTeX, SciPy and R code providing solutions to exercises in Elements of Statistical Learning (Hastie, Tibshirani & Friedman)
☆291Updated 11 years ago
Alternatives and similar repositories for Elements-of-Statistical-Learning:
Users that are interested in Elements-of-Statistical-Learning are comparing it to the libraries listed below
- ☆78Updated 8 years ago
- Compilation of resources found around the web connected with Machine Learning, Deep Learning & Data Science in general.☆94Updated 7 years ago
- Python notebooks for exercises covered in Stanford statlearning class (where exercises were in R).☆376Updated 9 years ago
- Python Version of BRML toolbox for Bayesian Reasoning and Machine Learning☆162Updated 9 years ago
- ☆82Updated 7 years ago
- Bayesian Machine Learning☆206Updated 2 years ago
- Repository of my thesis "Understanding Random Forests"☆525Updated 8 years ago
- Very concise notes on machine learning and statistics.☆379Updated 12 years ago
- Course materials for STA663☆37Updated 8 years ago
- STA663 Statistical Computing and Computation, Spring 2016☆86Updated 8 years ago
- Solutions to exercises from Machine Learning: A Probabilistic Perspective by Kevin P. Murphy☆47Updated 8 years ago
- Exercises for the book Applied Predictive Modeling by Kuhn and Johnson (2013)☆195Updated 7 years ago
- Code for Implementation, Inference, and Learning of Bayesian and Markov Networks along with some practical examples.☆106Updated 11 years ago
- Some Jupyter notebooks based on Bishop's "Pattern Recognition and Machine Learning" book☆76Updated 4 years ago
- useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html☆401Updated 6 years ago
- Introduction to Nonparametric Bayes, Infinite Mixture Models, and the Dirichlet Process (+ McDonald's)☆302Updated 9 years ago
- Work on Introduction to Statistical Learning☆121Updated 9 years ago
- Notes explaining Dirichlet Processes, HDPs, and Latent Dirichlet Allocation☆414Updated 5 years ago
- John K. Kruschke's Doing Bayesian Data Analysis: A Tutorial with R and BUGS☆113Updated 8 years ago
- Resources for STA 633 class☆165Updated 7 years ago
- A collection of tutorials on neural networks, using Theano☆223Updated last year
- Gradient boosted models☆106Updated 9 years ago
- Topics course Mathematics of Deep Learning, NYU, Spring 18☆540Updated last year
- Introduction to Empirical Bayes: Examples from Baseball Statistics☆188Updated 3 years ago
- my public kaggle code☆79Updated 11 years ago
- Advanced High Performance Data Science Toolbox for R by Laurae☆204Updated 7 years ago
- my blog☆267Updated 2 years ago
- Lecture Slides and R Sessions for Trevor Hastie and Rob Tibshinari's "Statistical Learning" Stanford course☆258Updated 6 years ago