Here’s a community-driven and educational guidance with current focus on machine learning and probabilistic AI – Metacademy. It will make a great assistance for those with strong interest and enthusiasm in machine learning, just like me. 🙂 It draws an intuitive map of sequential steps towards the concept you queried about. You’ll see once you give it a try.

# Machine Learning Subreddit

Machine Learning Subreddit with News, Research Papers, Videos, Lectures, Softwares and Discussions on:

- Machine Learning
- Data Mining
- Information Retrieval
- Predictive Statistics
- Learning Theory
- Search Engines
- Pattern Recognition
- Analytics

(quoted from Machine Learning Subreddit page)

# Machine Learning and Probabilistic Graphical Models Course

Machine Learning and Probabilistic Graphical Models Course provided by Department of Computer Science and Engineering, University at Buffalo.

# Machine Learning Video Library

A bunch of educational video segments on machine learning – Machine Learning Video Library.

Topics included:

Aggregation, Bayesian Learning, Bias-Variance Tradeoff, Bin Model, Data Snooping, Error Measures, Gradient Descent, Learning Curves, Learning Diagram, Learning Paradigms, Linear Classification, Linear Regression, Logistic Regression, Netflix Competition, Neural Networks, Nonlinear Transformation, Occam’s Razor, Overfitting, Radial Basis Functions, Regularization, Sampling Bias, Support Vector Machines, Validation, VC Dimension

# Journal of Machine Learning Research

Thanks to the first session by prof. Byoung-Tak Zhang (http://bi.snu.ac.kr/) in the ‘2014 PRML winter school’ programs, I luckily found the most important, probably, knowledge warehouse: JMLR. Better create your own account and take a deep look around there. 🙂

# [2014 PRML Winter School] [Day 3] [Session 4] Deep learning

TBD

# [2014 PRML Winter School] [Day 3] [Session 3] Statistical inference with graphical models

TBD