The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance. The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) mapping the problem on a general landscape of available ML methods, (2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and (3) successfully implementing a solution, and assessing its performance. The specialization is designed for three categories of students: · Practitioners working at financial institutions such as banks, asset management firms or hedge funds · Individuals interested in applications of ML for personal day trading · Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance. The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance.

In these free video courses, our PD team will teach you the basics of programming and teaching with the Hummingbird Bit Robotics Kit or the Finch Robot 2.0. These courses will also show you activities to inspire your own classroom integration and walk you through our free online resources. Each video course will take you approximately 2-3 hours to complete.

One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms. Prior coding or scripting knowledge is required.

In February 2022, researchers at the Data to AI (DAI) group at MIT released a paper called "The Need for Interpretable Features: Motivation and Taxonomy" [1]. In this post, I aim to summarize some of the main points and contributions of these authors and discuss some of the potential implications and critiques of their work. I highly recommend reading the original paper if you find any of this intriguing. Additionally, if you're new to Interpretable Machine Learning, I highly recommend Christopher Molnar's free book [2]. The core finding of the paper is that even with highly interpretable models like Linear Regression, non-interpretable features can result in impossible-to-understand explanations (ex. a weight of 4 on the feature x12 means nothing to most people).

Focal Systems is the industry leader in retail AI solutions. We are a Silicon Valley based startup, with operations in Canada, that has more than doubled in size every year since inception. Our mission is to automate and optimize brick and mortar retail using deep learning computer vision. We have been deployed at scale with the top retailers in the world. We are looking for smart, creative and passionate people who love to learn, enjoy thinking critically, and want to help build a great and enduring company!

Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent linguistic discrepancies in different languages could make answer spans predicted by zero-shot transfer violate syntactic constraints of the target language. In this paper, we propose a novel multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model (SSDM) to disassociate semantics from syntax in representations learned by multilingual pre-trained models. To explicitly transfer only semantic knowledge to the target language, we propose two groups of losses tailored for semantic and syntactic encoding and disentanglement. Experimental results on three multilingual MRC datasets (i.e., XQuAD, MLQA, and TyDi QA) demonstrate the effectiveness of our proposed approach over models based on mBERT and XLM-100. Code is available at:https://github.com/wulinjuan/SSDM_MRC.

We propose K-Deep Simplex (KDS) which, given a set of data points, learns a dictionary comprising synthetic landmarks, along with representation coefficients supported on a simplex. KDS integrates manifold learning and sparse coding/dictionary learning: reconstruction term, as in classical dictionary learning, and a novel local weighted $\ell_1$ penalty that encourages each data point to represent itself as a convex combination of nearby landmarks. We solve the proposed optimization program using alternating minimization and design an efficient, interpretable autoencoder using algorithm enrolling. We theoretically analyze the proposed program by relating the weighted $\ell_1$ penalty in KDS to a weighted $\ell_0$ program. Assuming that the data are generated from a Delaunay triangulation, we prove the equivalence of the weighted $\ell_1$ and weighted $\ell_0$ programs. If the representation coefficients are given, we prove that the resulting dictionary is unique. Further, we show that low-dimensional representations can be efficiently obtained from the covariance of the coefficient matrix. We apply KDS to the unsupervised clustering problem and prove theoretical performance guarantees. Experiments show that the algorithm is highly efficient and performs competitively on synthetic and real data sets.

An anonymous reader shares a report: ChatGPT's artificial intelligence generated dialogue has gotten pretty sophisticated -- to the point where it can write convincing sounding essays. So Edward Tian, a computer science student at Princeton, built an app called GPTZero that can "quickly and efficiently" label whether an essay was written by a person or ChatGPT. In a series of recent tweets, Tian provided examples of GPTZero in progress; the app determined John McPhee's New Yorker essay "Frame of Reference" to be written by a person, and a LinkedIn post to be created by a bot. On Twitter, he said he created the app over the holidays, and was motivated by the increasing possibility of AI plagiarism. Further reading: 1. OpenAI is developing a watermark to identify work from its GPT text AI; 2. OpenAI's attempts to watermark AI text hit limits; 3. A metadata'watermark' could be the solution to ChatGPT plagiarism fears.

The National Science Foundation (NSF) has designated the Center for Research in Computer Vision at the University of Central Florida (UCF) as a site for Research Experiences for Undergraduates (REU) in the area of Computer Vision for 2021-2023. The purpose of the REU is to encourage undergraduate students to pursue graduate school and research careers. UCF has continued to be an REU site in Computer Vision since the inception of REU by NSF in 1987. Through the longest REU program in the country, Dr. Shah and his colleagues have trained more than 300 REU students from more than 75 universities all over the USA, resulting in more than 80 high quality journal and conference publications. All previous REU participants successfully completed their degree in computer science related areas; about half have continued with graduate studies, several participants are now faculty members at different universities, and several participants have started their own companies.

It's no secret that the commercial application of NLP technologies has exploded in recent years. From chatbots and virtual assistants to machine translation and sentiment analysis, NLP technologies are now being used in a wide variety of applications across a range of industries. With the increasing demand for technologies that can process human language, investors have been eager to get a piece of the action. In this article, we look at NLP start-up funding over the past year, identifying the applications and domains that have received investment. A version of this article will appear in the Journal of Natural Language Engineering in early 2023.