Machine learning is ubiquitous and used to make critical business and life decisions every day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments. This session explores the fundamentals of machine learning using MATLAB . Rory reviews typical workflows for both supervised (classification and regression) and unsupervised learning, through examples. This presentation demonstrates examples of new functionality in Statistics and Machine Learning Toolbox and Neural Network Toolbox .

In a TEDx talk entitled "How Computers are Learning to Be Creative", Blaise Agüera y Arcas, Google principal scientist, demonstrated how neural networks recognizing images can run them in reverse-- thus generating them. Of this, he noted that perception and creativity are highly linked together. With Google's neural network models, machine perception and machine creativity are no longer that far-fetched. Arcas identified perception as the process by which simple objects are transformed by the mind into overwhelmingly different concepts. With today's technology, even computers are capable of perception. Creativity, on the other hand, is actually-- as far as Arcas is concerned in his speech-- the "flip side" of the former.

MLPB is meant to become an organized collection of machine learning problems and solutions. I need to classify something as A, B or C using a combination of numeric and categorical features. If I could find a similar problem, maybe I could modify the solution to work for my needs. This is where MLPB steps in. Want to see ML problems with sparse data?

Quick question....I've always wondered how people have time to compete on Kaggle AND work? Do you work full time as well and just dedicate your nights/weekend to competitions? I just finished an executive graduate program so will have more time outside of work but the winners seem to be doing this stuff full time.

In this study, we present and analyze a framework for geometric and topological estimation for mapping of unknown environments. We consider agents mimicking motion behaviors of cyborg insects, known as biobots, and exploit coordinate-free local interactions among them to infer geometric and topological information about the environment, under minimal sensing and localization constraints. Local interactions are used to create a graphical representation referred to as the encounter graph. A metric is estimated over the encounter graph of the agents in order to construct a geometric point cloud using manifold learning techniques. Topological data analysis (TDA), in particular persistent homology, is used in order to extract topological features of the space and a classification method is proposed to infer robust features of interest (e.g. existence of obstacles). We examine the asymptotic behavior of the proposed metric in terms of the convergence to the geodesic distances in the underlying manifold of the domain, and provide stability analysis results for the topological persistence. The proposed framework and its convergences and stability analysis are demonstrated through numerical simulations and experiments.

Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend $K$ most attractive items from a large set of $L$ candidate items. We propose two algorithms for solving this problem, which are based on the idea of linear generalization. The key idea in our solutions is that we learn a predictor of the attraction probabilities of items from their features, as opposing to learning the attraction probability of each item independently as in the existing work. This results in practical learning algorithms whose regret does not depend on the number of items $L$. We bound the regret of one algorithm and comprehensively evaluate the other on a range of recommendation problems. The algorithm performs well and outperforms all baselines.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

It's hard to believe, but one of the most important changes in the way people write in the last 50 years has been largely overlooked by historians of literature. The word processor--that is, any computer software or hardware used for writing, a nearly ubiquitous technology adopted by poets, novelists, graduate students, foreign correspondents, and CEOs--has never gotten its own literary history. Perhaps it was just too much under our noses--or, I suppose, in front of them. Now it finally has one. Five years ago, Matthew Kirschenbaum, an English professor at the University of Maryland, realized that no one seemed to know who wrote the first novel with the help of a word processor.

In June of 1975, the Office of the White House Press Secretary announced President Gerald R. Ford's picks for the National Medal of Science. One went to the Austrian-born mathematician and logician Kurt Gödel. Nicknamed Mr. Why by his parents, Gödel was known to a subset of his constituents as, simply, God. He received fan mail from all over the world, archiving it into files of "autograph requests," "inquiries from students and amateurs," "letters of appreciation," and "crank correspondence." A self-described "dunce fool of Mathematics" in West Bengal wrote seeking Gödel's "Guruship," and a svelte math teacher in California confessed that she'd taken the liberty of enlarging a photo of Gödel to make a poster for her classroom.

Imagine it's five minutes before a meeting. Your smartwatch, without prompting, sends you key points. While in the meeting, you take notes. Those notes are instantaneously absorbed by the system, then collated with relevant prior meetings, files and communications, in order to better prepare you for the next meeting. Born of the innovations of Big Data and possessed of a new net intelligence layer, self-learning software will have huge impacts on productivity across all departments of an enterprise.