I want to play around with twitter sentiment analysis and my main hesitation is on obtaining a dataset that's been classified for supervised learning. Do you know if a decent one exists already, or would I have to create one myself (or use mechical turk)?

Though there are many possible supervised learning model types to choose from, gradient boosted models (GBMs) are almost always my first choice. In many cases, they end up outperforming other options, and even when they don't, it's rare that a properly tuned GBM is far behind the best model. At a high level, the way GBMs work is by starting with a rough prediction and then building a series of decision trees, with each tree in the series trying to correct the prediction error of the tree before it. There's more detailed descriptions of the mechanics behind the algorithm out there, but this series of posts is intended to give more of an intuitive understanding of what the algorithm does. For this series, I'll be using a synthetic 2-dimensional classification dataset generated using scikit-learn's make_classification().

Structured prediction is used in areas such as computer vision and natural language processing to predict structured outputs such as segmentations or parse trees. In these settings, prediction is performed by MAP inference or, equivalently, by solving an integer linear program. Because of the complex scoring functions required to obtain accurate predictions, both learning and inference typically require the use of approximate solvers. We propose a theoretical explanation to the striking observation that approximations based on linear programming (LP) relaxations are often tight on real-world instances. In particular, we show that learning with LP relaxed inference encourages integrality of training instances, and that tightness generalizes from train to test data.

Boosting is an ensemble technique that attempts to create a strong classifier from a number of weak classifiers. In this post you will discover the AdaBoost Ensemble method for machine learning. This post was written for developers and assumes no background in statistics or mathematics. The post focuses on how the algorithm works and how to use it for predictive modeling problems. If you have any questions, leave a comment and I will do my best to answer.

Researchers from MIT's Computer Science and Artificial Laboratory (CSAIL) alongside machine learning-startup PatternEx have created a new cybersecurity defense system that makes use of both unsupervised and supervised learning methods. Human analysts are then presented with the data and given an opportunity to identify actual attacks, which are then fed back into the machine. The system learns and refines its accuracy over time. CSAIL research scientist Kalyan Veeramachaneni, one of AI,2's co-creators, described it this way: "The more attacks the system detects, the more analyst feedback it receives, which, in turn, improves the accuracy of future predictions. That human-machine interaction creates a beautiful, cascading effect."

I know supervised learning methods and basics of time series analysis, but what is a natural way to model prediction of events? Let's say I have created a lot time-dependent features. Every now and then, a particular event occurs, which is just a point event and not a value in my features. But I expect that before those events a signature in my features builds up, which indicates that the event is likely to occur some time in the future. The most basic thing would be to pick time steps, and at each step use supervised learning predict the probability that the event will occur within some time.

Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context, and successful approaches are known to benefit many applications in Natural Language Processing. Although supervised learning has been shown to provide superior WSD performance, current sense-annotated corpora do not contain a sufficient number of instances per word type to train supervised systems for all words. While unsupervised techniques have been proposed to overcome this data sparsity problem, such techniques have not outperformed supervised methods. In this paper, we propose a new approach to building semi-supervised WSD systems that combines a small amount of sense-annotated data with information from Word Sense Induction, a fully-unsupervised technique that automatically learns the different senses of a word based on how it is used. In three experiments, we show how sense induction models may be effectively combined to ultimately produce high-performance semi-supervised WSD systems that exceed the performance of state-of-the-art supervised WSD techniques trained on the same sense-annotated data. We anticipate that our results and released software will also benefit evaluation practices for sense induction systems and those working in low-resource languages by demonstrating how to quickly produce accurate WSD systems with minimal annotation effort.

You asked "which algorithm is the best?", but you answered "which algorithms should newcomers try first?". The first question is entirely problem dependent (and technically so is the second). However, experienced practitioners will generally know what to recommend for the second task at first blush, and would generally agree with your choices (some naive bayes, some linear models). I would also add random forest / boosted trees to that "which to try first" list. However, if I presented you with a timeseries problem all these methods would fall flat on their face without good feature engineering or a model that explicitly captures dependencies over samples. This is why it is problem dependent.

Gloria Lombardi speaks with software startup advisor Steve Ardire to explore the state of Artificial Intelligence (AI) and its implications for the future of work. In Part 1 of this post, we covered the meaning of AI vs. machine intelligence and how AI will affect the future of work. Now we turn our attention to what's going on in the AI market and what this technology could mean for healthcare in particular. AI technology is developing, fast. "In 2016," Ardire says, "we are already seeing the emergence of applications for human resources, marketing and communications, sales, customer service, market and risk intelligence and more."

Machine learning approaches to relation extraction are typically supervised and require expensive labeled data. To break the bottleneck of labeled data, a promising approach is to exploit easily obtained indirect supervision knowledge – which we usually refer to as distant supervision (DS). However, traditional DS methods mostly only exploit one specific kind of indirect supervision knowledge – the relations/facts in a given knowledge base, thus often suffer from the problem of lack of supervision. In this paper, we propose a global distant supervision model for relation extraction, which can: 1) compensate the lack of supervision with a wide variety of indirect supervision knowledge; and 2) reduce the uncertainty in DS by performing joint inference across relation instances. Experimental results show that, by exploiting the consistency between relation labels, the consistency between relations and arguments, and the consistency between neighbor instances using Markov logic, our method significantly outperforms traditional DS approaches.