Other courses and community
01 Community courses
These stand-alone courses in advanced ML topics are aimed at DS professionals with extensive pre-existing knowledge and focus on state-of-the-art tools and developments often unavailable in academic courses and online learning platforms. These courses are offered free, for the enrichment and benefit of ML community, and provide in-depth understanding of high-level topics, combining theoretical foundations and practical knowledge taught from a professional point of view.
Adversarial learningLecturer: Ziv Katzir
Adversarial machine learning (AL) is a relatively new and extremely active research domain, focused on understanding the susceptibility of machine learning algorithms to misleading inputs.This course is a journey through the evolution of adversarial machine learning in recent years. It starts with the early methods of attack and defense, and concludes with recent discoveries and outstanding research questions. As part of this journey we will review notable studies, and discuss their contribution to the understanding of this phenomenon.
Advanced Time SeriesLecturer: Gleb Ivashkevich
The course is dedicated to deep learning techniques in time series modelling. Deep learning models offer significant advantages when applied to time series problems, regardless of the task (regression, classification, etc). The course is centered around a set of seminal research papers, several datasets and known use cases. Since there exists no well-established curriculum in the domain of deep learning for time series, we'll use this unique structure to bring together academic innovation and industry experience.
02 Practicum by Yandex
Practicum is a fully online bootcamp for self-driven individuals who want to enter the tech market. This is an online education service that allows a wide range of people to learn a new and high-demand profession from scratch. Practicum comes with 24/7 community support and mentoring, and takes a practical and project-based approach including continuous hands-on use of the studied technologies.
Collect, analyze and visualize data. Over the course of this six-month, 20-hour-per-week program, you will master the skills required to become a data analyst and build a portfolio of projects on topics such as these: User preferences for streaming on-demand media, the effect of weather on taxi services and boosting e-commerce revenue
Data ScientistLearn to work with large volumes of data and discern patterns using Machine Learning. During the 8 months, 20 hour per week training course, you will master the skills required to become a data scientist and build a portfolio of projects including: prediction of traffic jams for navigation services, selecting which ad to display for each user, and predicting busy times for courier services.
Engage in front-end development as well as back-end basics over the course of this 10-month program. Set out over a series of two-week sprints and consisting of around 20 hours of work a week, and build a solid foundation of knowledge, skills, and portfolio items required to become an entry-level web developer.
03 Data skills
Data skills courses are a short and intensive mini-bootcamp, aimed at tech professionals with diverse experience and some coding background, but no previous DS experience. These courses aim to provide an opening to the worlds of Data Science and Artificial Intelligence by offering an entry-level perspective on a wide range of DS and ML topics. The course provides hands-on experience with multiple common DS tools, offering practical experience and understanding of core ML tasks such as classification, regression, and clustering, as well as overview of the capabilities of Deep Learning and state-of-the-art developments
Intro to Data ScienceIn collaboration with: AEAI (Architects and Engineers Association in Israel)
Many companies have the facilities to gather and analyse data and utilise it for making business-related decisions. However, there are multiple obstacles for incorporating Data Science tools and using Data Science to its full potential – mostly due to existing gaps in knowledge and understanding. Y-DATA data skills courses offer the tools needed to use data to its full potential – Data exploration, manipulation, visualisation, modelling, prediction and classification. The course aims to provide the knowledge and tools required to analyse data and extract insights immediately upon completion, including: formulating goals and planning approach, required resources and independent ability to use relevant Python libraries. The course emphasises hands-on practice and puts practical exercises at the core of the learning.
04 Academic courses
These are introductory academic courses on ML topics as part of undergraduate studies offered by Y-DATA faculty and based on our expertise in teaching DS topics with applicable tools and skillset. The courses allow students of not computer/engineering degrees to gain basic understanding of DS tools and their potential, as well as providing understanding of key data manipulation skills for research and hypothesis testing. The courses combine practice of common DS tools with glimpses into the full potential of ML
Introduction to Data Science for Product ManagementLecturer: Adir Solomon
Data is quickly becoming our world’s most valuable commodity. As its importance grows, it’s never been more important to explore, understand and communicate data concepts as part of one’s daily work. This course offers the necessary tools to use data to its full potential and to obtain insights needed from the position of a product manager. The course covers 4 separate modules across 3 verticals: product, management, and data science.
Y-DATA leads an ongoing meetup series open for anybody who is interested in the technical aspects of Data Science and Machine Learning. The main goal of these events is to build stronger academia-industry relationships and to make our ecosystem richer by bringing together top names from the tech industry in Israel and abroad and building a community around the opportunity to meet in an informal setting and engage in exchange of ideas as well as build up professional networks.
Y-DATA#18: ML Methods for Video AnalysisDate: Wednesday, March 18, 2021 6:00 PM
Two great talks about video analysis in real life and recent ML methods used in this field. Our speakers will be Alex Rav-Acha (VP Engineering at Vimeo) and Sergey Ovcharenko (Yandex)
Y-DATA#17: Deep Learning Algorithms - From Theory to IndustryDate: Wednesday, January 20, 2021 6:00 PM
Abstract: Chest radiography is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. However, there is an immense world-wide decrease in the number of physicians capable of providing their rapid and accurate interpretation. With 2 billion people joining the middle class worldwide and a growing global shortage of clinical experts, there is a sense of urgency for the development of technologies which can help bridge the gap between the supply and demand of radiology services. Here we will review the research and development of Zebra-Medical's AI-based solutions aimed at providing automated and scalable diagnostic support in the interpretation of chest radiographs. We'll demonstrate the application of our technology on real-life clinical examples where they have impacted patients' care by substantially reducing time to treatment and preventing misdiagnosis.
Y-DATA#16: How to find and engage with academic papersDate: Wednesday, December 30, 2020 6:00 PM
Intro: The number of new published papers in this field is booming during last 10 years and it's not easy to cope with it and stay up-to-date, even within a specific narrow domain of interest. Eddie Smolyansky co-created Connected Papers to solve some common problems he encountered with his research teams, trying to identify relevant papers to read. In this presentation, he'll share highlights from his research on how to efficiently identify and consume academic papers. After that Eddie will demonstrate Connected Papers, a free tool that has amassed a big following within months of its launch in June 2020. In the last part of the meetup he'll share the vision on future opportunities for tools engaging with scientific literature (spoiler: it's social).