mackelab / machine-learning-I
Slides and lecture notes for the course 'machine learning I' taught at the Graduate School Neural Information Processing in Tuebingen.
☆30Updated 9 years ago
Alternatives and similar repositories for machine-learning-I:
Users that are interested in machine-learning-I are comparing it to the libraries listed below
- Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018☆22Updated 6 years ago
- Experiments in Bayesian Machine Learning☆69Updated 5 years ago
- Courses and practical sessions for the Optimal Transport and Machine learning course at Statlearn 2018☆26Updated 6 years ago
- Repo to accompany paper "Implicit Self-Regularization in Deep Neural Networks..."☆43Updated 6 years ago
- Materials for Bayesian Methods in Machine Learning Course☆88Updated 4 months ago
- ☆29Updated 6 years ago
- Material for my Caltech tutorial on deep learning and tensor methods☆70Updated 6 years ago
- ☆30Updated 4 years ago
- Short Course on Optimization for Machine Learning - Slides and Practical Lab - Pre-doc Summer School on Learning Systems, July 3 to 7, 20…☆18Updated 7 years ago
- Reducing Reparameterization Gradient Variance code.☆33Updated 7 years ago
- ☆14Updated 4 years ago
- This code accompanies the proximity variational inference paper.☆18Updated 6 years ago
- This repository houses the code for the community website http://www.probabilistic-numerics.org☆35Updated 4 years ago
- ☆64Updated 6 years ago
- Hidden Markov Models (HMMs) with tied states and autoregressive observations☆23Updated 4 years ago
- Code for the Santa algorithm for deep learning☆17Updated 6 years ago
- Repo for a paper about constructing priors on very deep models.☆73Updated 8 years ago
- Variational Fourier Features☆84Updated 3 years ago
- Code for "Towards a learning theory of cause-effect inference" (ICML 2015).☆29Updated 4 years ago
- Open access book on variational Bayesian methods written collaboratively☆28Updated 9 years ago
- Practice with MCMC methods and dynamics (Langevin, Hamiltonian, etc.)☆42Updated 5 years ago
- Slides and notebooks for the IfI Summer School 2018 on Machine Learning☆36Updated 6 years ago
- Self-study notes for Indian Buffet Process, from reading through "The Indian Buffet Process: An Introduction and Review", Griffiths, Ghah…☆11Updated 8 years ago
- Contains all materials for the paper "A counterfactual simulation model of causal judgment".☆24Updated 3 years ago
- PyData San Luis 2017 Tutorial: An Introduction to Gaussian Processes in PyMC3☆15Updated 7 years ago
- Implementation of Stochastic Gradient MCMC algorithms☆41Updated 8 years ago
- ☆25Updated 2 years ago
- Python 3.7 version of David Barber's MATLAB BRMLtoolbox☆24Updated 6 years ago
- Talks from Neil Lawrence☆54Updated last year
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago