MALSAR: Multi-Task multiple machine Learning via Structural Regularization Matlab toolkit. One class ML Machined Learnings: P-U Learning and Pointwise Classification. Once you become aware of something you see it everywhere; in particular, once I learned about P-U problems I started seeing examples of people (implicitly!)
Treating P-U datasets like ordinary datasets. Surprisingly this seems to generally work out, and I found a paper by Elkan and Noto called Learning Classifiers from Only Positive and Unlabeled Data which helps explain why. In the Elkan and Noto model, there is a distribution D on X \times \{ 0, 1 \} sampling from which would constitute a traditional data set. However instead we sample from a distribution D^\prime on X \times \{ 0, 1 \} defined via Draw (x, y) \sim D. Caveats aside, the above observation model yields some interesting results. I.e., the probability of a positive label in the observed data set is proportional to the probability of a positive label in the underlying unobserved data set.
Elkan and Noto uncover several other interesting implications of the above observation model. Metacademy - Coursera Machine Learning Supplement. Metacademy is an open source platform designed to help you efficiently learn about any topic that you're interested in---it currently specializes in machine learning and artificial intelligence topics.
The idea is that you click on a concept that interests you, and Metacademy produces a "learning plan" that will help you learn the concept and all of its prerequisite concepts that you don't already know. Metacademy's learning experience revolves around two central components: You can tell Metacademy that you understand a [prerequisite] concept by clicking the checkmark next to the concept's title in the graph or list view. Furthermore, you can then click the "hide" button in the upper right to hide the concepts you understand (Metacademy remembers the concepts you've learned, so it'll automatically apply these in the future). Coursera Roadmap. Predicting Stock Prices Using Technical Analysis Machine Learning.
Classification - Feature selection for "final" model when performing cross-validation in machine learning. 'Machine Learning books' Practical Machine Learning Lecture: Feature selection Berkeley Sources Case Studies Machine learning and Feature Selection Data science methods. Artificial Intelligence in Motion: machine learning. Hi all, Recently I've been working with recommender systems and association analysis.
This last one, specially, is one of the most used machine learning algorithms to extract from large datasets hidden relationships. The famous example related to the study of association analysis is the history of the baby diapers and beers. This history reports that a certain grocery store in the Midwest of the United States increased their beers sells by putting them near where the stippers were placed.
In fact, what happened is that the association rules pointed out that men bought diapers and beers on Thursdays. Association analysis is the task of finding interesting relationships in large data sets. A list of transactions from a grocery store is shown in the figure above. How do we define these so called relationships ? The support and confidence are ways we can quantify the success of our association analysis. To sum up, one example of rule extracted from associatio analysis: Apriori Algorithm.
S. M. Ali Eslami / Patterns for Research in Machine Learning - Checkpoints must dos. This page lists a handful of code patterns that I wish I was more aware of when I started my PhD . Each on its own may seem pointless, but collectively they go a long way towards making the typical research workflow more efficient. And an efficient workflow makes it just that little bit easier to ask the research questions that matter. My guess is that these patterns will not only be useful for machine learning, but also any other computational work that involves either a) processing large amounts of data, or b) algorithms that take a significant amount of time to execute.
Disclaimer: The ideas below have resulted from my experiences working with MATLAB. Other IDEs, languages or frameworks may have better solutions for the kinds of problems that I'm trying to address. Here they are: Use version control. Separate code from data. Separate input data, working data and output data. Modify input data with care. Save everything to disk frequently. Separate options from parameters.
Use checkpointing. Extreme Learning Machines - ML methods site ELM.