Machine Learning (ML) is one of the hot buzzwords these days, but even though EDA deals with big-data types of issues it has not made much progress incorporating ML techniques into EDA tools. Many EDA problems and solutions are statistical in nature, which would suggest a natural fit. So why is it so slow to adopt machine learning technology, while other technology areas such as vision recognition and search have embraced it so easily? "You can smell a machine learning problem," said Jeff Dyck, vice president of technical operation for Solido Design Automation. "We have a ton of data, but which methods can we apply to solve the problems? That is the hard part. You cannot open a text book or take a course and apply those methods to solve all problems. Engineering problems require a different angle."

A Reddit study group for the free online version of the Stanford class "Machine Learning", taught by Andrew Ng. The purpose of this reddit is to help each other understand the course materials, not to share solutions to assignments. Please follow the Stanford Honor Code. I'm a new user to Reddit, how does this site work? I have a question about the (class / videos / quiz / homework), how can I get help?

By lifting the ReLU function into a higher dimensional space, we develop a smooth multi-convex formulation for training feed-forward deep neural networks (DNNs). This allows us to develop a block coordinate descent (BCD) training algorithm consisting of a sequence of numerically well-behaved convex optimizations. Using ideas from proximal point methods in convex analysis, we prove that this BCD algorithm will converge globally to a stationary point with R-linear convergence rate of order one. In experiments with the MNIST database, DNNs trained with this BCD algorithm consistently yielded better test-set error rates than identical DNN architectures trained via all the stochastic gradient descent (SGD) variants in the Caffe toolbox.

The partially observable hidden Markov model is an extension of the hidden Markov Model in which the hidden state is conditioned on an independent Markov chain. This structure is motivated by the presence of discrete metadata, such as an event type, that may partially reveal the hidden state but itself emanates from a separate process. Such a scenario is encountered in keystroke dynamics whereby a user's typing behavior is dependent on the text that is typed. Under the assumption that the user can be in either an active or passive state of typing, the keyboard key names are event types that partially reveal the hidden state due to the presence of relatively longer time intervals between words and sentences than between letters of a word. Using five public datasets, the proposed model is shown to consistently outperform other anomaly detectors, including the standard HMM, in biometric identification and verification tasks and is generally preferred over the HMM in a Monte Carlo goodness of fit test.

I have this portfolio with jupyter notebooks done by me. Several of them need to be reworked or deleted, but most of them are okay. One of them is similar to things which I did while I worked in a bank. As for the first project - this is my attempt to build a site with handwritten digit recognition system with online training. This portfolio really helped me when I was looking for a job.

Across the world, higher education is increasingly being judged through the lens of employability. More and more, politicians are asking universities how they are preparing students for work, and even tying their funding to their graduates' success in the workplace. In the West, this has mainly been a result of the squeeze on the public purse and – in some countries, at least – an accompanying rise in tuition fees. But there is also growing anxiety about the technological revolution's potential to replace large numbers of human workers with computers and robots if humans can't keep one step ahead in the race to acquire skills. So how well are universities meeting the challenge of preparing graduates for the digital age?

Mitchell Cooper likes to build things, and not just chemistry in the water as a senior captain for the Corona del Mar High boys' water polo team. Cooper is the president of the CdM Robotics club. Now that the boys' water polo season is over, he will turn his attention to that after the holidays. Members of the club compete in the For Inspiration and Recognition of Science and Technology (FIRST) robotics competition. "We build 120-pound robots to compete in a game that's announced to us in January of each year," Cooper said.

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Includes: 40.5 hours on-demand video 20 Articles 2 Supplemental Resources Full lifetime access Access on mobile and TV Certificate of Completion Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

We propose a framework, named Aggregated Wasserstein, for computing a dissimilarity measure or distance between two Hidden Markov Models with state conditional distributions being Gaussian. For such HMMs, the marginal distribution at any time position follows a Gaussian mixture distribution, a fact exploited to softly match, aka register, the states in two HMMs. We refer to such HMMs as Gaussian mixture model-HMM (GMM-HMM). The registration of states is inspired by the intrinsic relationship of optimal transport and the Wasserstein metric between distributions. Specifically, the components of the marginal GMMs are matched by solving an optimal transport problem where the cost between components is the Wasserstein metric for Gaussian distributions. The solution of the optimization problem is a fast approximation to the Wasserstein metric between two GMMs. The new Aggregated Wasserstein distance is a semi-metric and can be computed without generating Monte Carlo samples. It is invariant to relabeling or permutation of states. The distance is defined meaningfully even for two HMMs that are estimated from data of different dimensionality, a situation that can arise due to missing variables. This distance quantifies the dissimilarity of GMM-HMMs by measuring both the difference between the two marginal GMMs and that between the two transition matrices. Our new distance is tested on tasks of retrieval, classification, and t-SNE visualization of time series. Experiments on both synthetic and real data have demonstrated its advantages in terms of accuracy as well as efficiency in comparison with existing distances based on the Kullback-Leibler divergence.

Effective techniques for analyzing and detecting changes in streaming data, especially in the era of big data, pose new challenges to the machine learning and the statistics community [1], [2]. As a result, early approaches for detecting statistical changes in a time series (such as change point detection), have had to be extended for online detection of changes in a multivariate data streams [3], [4]. Some of these techniques for detecting the intrinsic change in the relationship of the incoming data streams have been applied to numerous real-world applications, such as fraud detection, user preference prediction and email filtering, [5], [6]. Online classification is another common task performed on streaming multivariate time series data that takes advantage of these statistical relationships to predict a class label at each time index [7]. If the underlying source generating the data is not stationary, the optimal decision rule for the classifier would change over time - a phenomena known as concept drift [8]. Given the impact of concept drift on the predictive performance of an online classifier, there is a need to detect these concept drifts as early as possible. The inability of change point detection approaches to detect these concept drifts, has motivated the need for concept drift detection approaches that not only monitor the join distribution of a multivariate data stream but also changes in its relationship to the class labels of the streaming data. Shujian Yu and José C. Príncipe are with the Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.