Inductive learning, or induction, is the process of creating generalizations from individual instances.
Ian Ozsvald Saturday 15:00 Assembly Room Diagnosing, explaining and scaling machine learning is hard. I'll talk about a set of libraries that have helped me to understand when and how a model is failing, helped me communicate why it is working to non-technical users, automated the search for better models and helped me to scale my modeling. These libraries will make it more likely that you deliver trustworthy and reliable systems that will actually make it past R&D and into Production. The talk will be rooted in my experience delivering client projects and participating in Kaggle competitions.
Can a computer automatically detect pictures of shirts, pants, dresses, and sneakers? It turns out that accurately classifying images of fashion items is surprisingly straight-forward to do, given quality training data to start from. Supervised learning, in particular for classification, is a popular topic amongst artificial intelligence and machine learning enthusiasts. It's common for developers to utilize a well known and easy to process dataset for their first attempts at using supervised learning. The MNIST dataset is an example of such a source, providing thousands of examples of handwritten digits that can be used for supervised learning with your machine learning algorithms.
This package contains implementations of the Relief family of feature selection algorithms. It is still under active development and we encourage you to check back on this repository regularly for updates. These algorithms excel at identifying features that are predictive of the outcome in supervised learning problems, and are especially good at identifying feature interactions that are normally overlooked by standard feature selection methods. The main benefit of Relief algorithms is that they identify feature interactions without having to exhaustively check every pairwise interaction, thus taking significantly less time than exhaustive pairwise search. Relief algorithms are commonly applied to genetic analyses, where epistasis (i.e., feature interactions) is common.
Starting with the Google DeepMind paper, there has been a lot of new attention around training models to play video games. You, the data scientist/engineer/enthusiast, may not work in reinforcement learning but probably are interested in teaching neural networks to play video games. With that in mind, here's a list of nuances that should jumpstart your own implementation. The lessons below were gleaned from working on my own implementation of the Nature paper. The lessons are aimed at people who work with data but may run into some issues with some of the non-standard approaches used in the reinforcement learning community when compared with typical supervised learning use cases.
Supervised learning is the Data mining task of inferring a function from labeled training data.The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called thesupervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way.
This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree. The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff. This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS).
Amazon's Mechanical Turk is a platform for soliciting work on online tasks that has been used by market researchers, translators, and data scientists to complete surveys, perform work that cannot be easily automated, and create human-labeled data for supervised learning systems. Because of the role crowdwork plays as a source of the human knowledge that machine intelligence relies on to train algorithms, a better understanding how crowdworking platforms like mTurk function as a conduit for human intelligence can improve its usefulness for the data scientists that rely on it. Rather than exploring that side of the crowdworking experience, I tried to focus my attention on tasks that looked like they were intended to support machine learning (rather than the various other services that mTurk supports, like psychological profiles, market research surveys, or translation tasks), and found that the design of mTurk HITs have important consequences for data scientists concerned with producing useful labeled data. No matter how extensible the task-building platform is, there are only a few ways for task designers to elicit information from task workers: Writing text in fields, selecting radio buttons or checkboxes, or using dropdown menus are the most database-friendly methods, but recording audio, capturing video or still photos from a webcam, or asking for drawn annotations may also be used.
In this post, I will show how a simple semi-supervised learning method called pseudo-labeling that can increase the performance of your favorite machine learning models by utilizing unlabeled data. First, train the model on labeled data, then use the trained model to predict labels on the unlabeled data, thus creating pseudo-labels. In competitions, such as ones found on Kaggle, the competitor receives the training set (labeled data) and test set (unlabeled data). Pseudo-labeling allows us to utilize unlabeled data while training machine learning models.
Possibly on of the most important parts of building an effective Artificial Intelligence is to feed it information from diverse data sources. Supervised learning techniques have built artificially intelligent software that can provide in depth business analytics, predict consumer behaviour, translate different languages, read emotions, drive a car and, of course, play chess. In HealthTech, health trackers and virtual doctors could account for patients' emotional state, improving customer experience. Is DeepMind's study a step towards technological singularity?