Bayesian Inference lies at the very core of machine learning, decision making theory, pattern recognition, etc. Plus there are so many new ideas, variations, frameworks and applications keep coming out of the original Bayesian inference in many field especially in machine learning and statistical pattern recognition. Hence I think it might be a good idea to gather some interesting contributions of the main concept in one place. Nevertheless it is almost-surely not possible to put all of the works here completely in one or two days, so please bare with me. I will keep update this post as time goes by. Hopefully one day soon I can make this post useful for all people both beginner and pratitioners who want to work on Bayesian inference and learning.
- Bayesian Inference: Good tutorials
- Sparse Bayesian Model (with RVM) – By Mike Tipping
- Bayesian Inference with Active Learning – Larry Carin
- Bayesian Compressive Sensing – Larry Carin
- Variational Bayes
Conference and Workshops
- Click here to see the list
- TPAMI: The IEEE Transactions on Pattern Analysis and Machine Intelligence
- JMLR: Journal of Machine Learning Research
- JAIR: Journal of Artificial Intelligence Research
- Machine Learning (Springer)
Websites and Blogs
- Videolectures.net: This is a great website for machine learning enthusiasms. Tons of lectures and tutorials, all in video format. Updated regularily. You can find all interesting lectures from a lot of conferences, workshops and MLSS here.
- NIPS: Here you can find tons of papers and tutorial videos.
Also I found a journal/conference list in Bob Wang’s homepage very helpful.