The focus of this post is to share a comprehensive list of resources for women and non-binary people in data science for the goal of increasing diversity in the workplace. Some positive news is that research has shown that networking events for women do indeed move the needle on equality. If you invite me to speak at your conference, be prepared to get this answer. I invite other men to do the same.#diversity Below are various groups in the data and tech space.

About this course: This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.

Many parametric statistical models are not properly normalised and only specified up to an intractable partition function, which renders parameter estimation difficult. Examples of unnormalised models are Gibbs distributions, Markov random fields, and neural network models in unsupervised deep learning. In previous work, the estimation principle called noise-contrastive estimation (NCE) was introduced where unnormalised models are estimated by learning to distinguish between data and auxiliary noise. An open question is how to best choose the auxiliary noise distribution. We here propose a new method that addresses this issue. The proposed method shares with NCE the idea of formulating density estimation as a supervised learning problem but in contrast to NCE, the proposed method leverages the observed data when generating noise samples. The noise can thus be generated in a semi-automated manner. We first present the underlying theory of the new method, show that score matching emerges as a limiting case, validate the method on continuous and discrete valued synthetic data, and show that we can expect an improved performance compared to NCE when the data lie in a lower-dimensional manifold. Then we demonstrate its applicability in unsupervised deep learning by estimating a four-layer neural image model.

Diffusions and related random walk procedures are of central importance in many areas of machine learning, data analysis, and applied mathematics. Because they spread mass agnostically at each step in an iterative manner, they can sometimes spread mass "too aggressively," thereby failing to find the "right" clusters. We introduce a novel Capacity Releasing Diffusion (CRD) Process, which is both faster and stays more local than the classical spectral diffusion process. As an application, we use our CRD Process to develop an improved local algorithm for graph clustering. Our local graph clustering method can find local clusters in a model of clustering where one begins the CRD Process in a cluster whose vertices are connected better internally than externally by an $O(\log^2 n)$ factor, where $n$ is the number of nodes in the cluster. Thus, our CRD Process is the first local graph clustering algorithm that is not subject to the well-known quadratic Cheeger barrier. Our result requires a certain smoothness condition, which we expect to be an artifact of our analysis. Our empirical evaluation demonstrates improved results, in particular for realistic social graphs where there are moderately good---but not very good---clusters.

Visual question answering (Visual QA) has attracted a lot of attention lately, seen essentially as a form of (visual) Turing test that artificial intelligence should strive to achieve. In this paper, we study a crucial component of this task: how can we design good datasets for the task? We focus on the design of multiple-choice based datasets where the learner has to select the right answer from a set of candidate ones including the target (\ie the correct one) and the decoys (\ie the incorrect ones). Through careful analysis of the results attained by state-of-the-art learning models and human annotators on existing datasets, we show that the design of the decoy answers has a significant impact on how and what the learning models learn from the datasets. In particular, the resulting learner can ignore the visual information, the question, or both while still doing well on the task. Inspired by this, we propose automatic procedures to remedy such design deficiencies. We apply the procedures to re-construct decoy answers for two popular Visual QA datasets as well as to create a new Visual QA dataset from the Visual Genome project, resulting in the largest dataset for this task. Extensive empirical studies show that the design deficiencies have been alleviated in the remedied datasets and the performance on them is likely a more faithful indicator of the difference among learning models. The datasets are released and publicly available via http://www.teds.usc.edu/website_vqa/.

Three-year olds are known for a long list of bad habits: biting other kids, throwing toys at their mom and answering every question with "no." Despite those irrational behaviours, they are also smart. Show a three-year-old girl a van, a truck and a car, and she will quickly learn to identify the three types of vehicles. Digvir Jayas, vice-president of research at the University of Manitoba and grain storage expert, said computers aren't as smart as three- year olds, at least when it comes to computer vision and identifying objects. But scientists are now teaching computers to think like a three-year old, so the machines can see the differences between one object and another.

The facial-recognition cameras installed near the bounce houses at the Warehouse, an after-school recreation center in Bloomington, Indiana, are aimed low enough to scan the face of every parent, teenager and toddler who walks in. The center's director, David Weil, learned earlier this year of the surveillance system from a church newsletter, and within six weeks he had bought his own, believing it promised a security breakthrough that was both affordable and cutting-edge. Since last month, the system has logged thousands of visitors' faces – alongside their names, phone numbers and other personal details – and checked them against a regularly updated blacklist of sex offenders and unwanted guests. The system's Israeli developer, Face-Six, also promotes it for use in prisons and drones. "Some parents still think it's kind of '1984,' " said Weil, whose 21-month-old granddaughter is among the scanned.

If companies fail to make artificial intelligence (AI) a core competency within the next five years, they will face either stagnation or elimination. Recent GlobalData research reveals that incumbents in virtually every industry are facing some kind of game-changing disruption from AI technologies, with some being better prepared than others for the challenges ahead. AI adoption is highest in the banking and financial services, automotive, technology and telecoms verticals while the construction, energy and education industries lag behind. The report also identifies the market leaders and notable disruptive start-ups across seven AI technology areas, namely; machine learning, data science, conversational platforms, computer vision, AI chips, smart robots and context-aware computing. "AI is finally beginning to have an impact on the global economy. This technology has the potential to transform how we live and work at an extremely rapid rate and it is already beginning to have an effect across industries, with well-established incumbents increasingly coming up against major disruption from AI platforms developed either by the tech giants (including Amazon, Google and Microsoft) or by AI-focused start-ups," said Ed Thomas, Senior Analyst, Thematic Research Technology at GlobalData.

This course helps to understand what AI is, how it works, and how to use it to build smart apps. You can learn how to build simple machine learning models and implement conversational bots. To learn about machine learning, enter this course to get both theoretical and practical knowledge. You will understand various concepts such as inductive bias, the PAC and Mistake-bound learning frameworks, minimum description length principle, and Ockham's Razor.

Google has chosen India as a major battleground to take on rivals Amazon and Microsoft in its bid to dominate cloud computing services, said Rick Harshman, MD-Asia Pacific for Google Cloud, in an interview. He said the company had made big strides in the country in terms of enterprises adopting its technologies such as cloud services, security, artificial intelligence and machine learning. How are Indian enterprises adopting your technologies, especially cloud and artificial intelligence? How large is the opportunity? Globally... only about 5%-10% of all workloads in IT run on the cloud. I think the estimates are quite conservative.