The NBA Finals are over. The last of the champagne bottles have been emptied and the confetti has begun to settle. Now that the Golden State Warriors have finished unleashing their otherworldly dominance on the basketball world, I thought it would be a good time to wrap up a hardwood-focused machine learning project. The Warriors are prime exhibitors of a new trend in the sport of basketball, a trend that advocates pass-first, ballet-beautiful movement over dominance through individual greatness. As such, traditional positions like'point guard' and'center' really don't seem to apply to their players anymore.

This resource is designed primarily for beginning data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?" Even an experienced data scientist cannot tell which algorithm will perform the best before trying different algorithms. We are not advocating a one and done approach, but we do hope to provide some guidance on which algorithms to try first depending on some clear factors. The machine learning algorithm cheat sheet helps you to choose from a variety of machine learning algorithms to find the appropriate algorithm for your specific problems.

Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function to transform a deterministic input $y'$ to a sample $\mathbf{x}$. Our BC-GANs extend traditional GANs to a Bayesian framework, and naturally handle unsupervised learning, supervised learning, and semi-supervised learning problems. Experiments show that the proposed BC-GANs outperforms the state-of-the-arts.

I just want to emphasise for any readers that the density ratio approximation is tight only when the discriminator is optimal. Given that they update the discriminator even less than the generator they are essentially optimising a quite loose approximation to a likely loose bound on the data log likelihood.

In many situations where a classifier is to be learned, it is easy to collect unlabeled data but costly to obtain labels. This has motivated the pool-based active learning model, in which a learner has access to a collection of unlabeled data points and is allowed to ask for individual labels in an adaptive manner. The hope is that choosing these queries intelligently will rapidly yield a low-error classifier, much more quickly than with random querying. A central focus of active learning is developing efficient querying strategies and understanding their label complexity. Over the past decade or two, there has been substantial progress in developing such rigorously-justified active learning schemes for general concept classes. For the most part, these schemes can be described as mellow: rather than focusing upon maximally informative points, they query any point whose label cannot reasonably be inferred from the information received so far.

This is a two-part series about using machine learning to hack my taste in music. In this first piece, I applied unsupervised learning techniques and tools on Pandora data to analyze songs that I like. The second part, which will be published soon, is about using supervised on Spotify data to predict whether or not I will like a song. If you take a look at my top tracks on Last.FM, you'll notice a smorgasbord of tracks from artists like LCD Soundsytem, Jimi Hendrix, and Kanye West. When I make a playlist, it's not uncommon for me to include some 80's post-disco, 2000s indie rock, and Nigerian or Turkish funk.

I feel rather uneasy at this application of DCGANs. Yes, we know they are great at hallucinating details and creating single plausible reconstructions of high perceptual detail, so the perceptual ratings do not surprise anyone, but that's not the same thing as making accurate diagnoses, is it?

As artificial intelligence and machine learning start to make their mark on healthcare in a big way, there's no shortage of hype. But there's also no small amount of uncertainty about just what it all means – literally. "We haven't settled on how to talk about this yet, and it's creating confusion in the market," said Leonard D'Avolio, assistant professor in the Brigham and Women's Division of General Internal Medicine and Primary Care (part of Harvard Medical School), and CEO of machine learning company Cyft. "If I describe what I do as cognitive computing, but a competitor describes what they do as AI or machine learning or data mining, it's hard to even understand what problems we are trying to solve." Because the problems that can be solved in healthcare with AI are numerous and notable, said Zeeshan Syed, director of the clinical inference and algorithms program at Stanford Health Care – whether it's better decision support at the bedside, better business intelligence for the C-suite or big-picture challenges such as managing care "across complex networks of providers for complex populations and complex diseases."

Hui Li is Principal Staff Scientist, Data Science at SAS. This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?" Even an experienced data scientist cannot tell which algorithm will perform the best before trying different algorithms. We are not advocating a one and done approach, but we do hope to provide some guidance on which algorithms to try first depending on some clear factors.

Transferring characteristics from one image to another is an exciting proposition. How cool would it be if you could take a photo and convert it into the style of Van Gogh or Picasso! Or maybe you want to put a smile on Agent 42's face with the virally popular Faceapp These are examples of cross domain image transfer - we want to take an image from an input domain $D_i$ and then transform it into an image of target domain $D_t$ without necessarily having a one-to-one mapping between images from input to target domain in the training set. Relaxation of having one-to-one mapping makes this formulation quite powerful - the same method could be used to tackle a variety of problems by varying the input-output domain pairs - performing artistic style transfer, adding bokeh effect to phone camera photos, creating outline maps from satellite images or convert horses to zebras and vice versa!! This is achieved by a type of generative model, specifically a Generative Adversarial Network dubbed CycleGAN by the authors of this paper.