Course Overview This is a class for computer-literate people with no programming background who wish to learn basic Python programming. The course is aimed at those who want to learn data wrangling - manipulating downloaded files to make them amenable to analysis. We concentrate on language basics such as list & string manipulation, control structures, simple data analysis packages, & introduce modules for downloading data from the web. Instructors Tony Schultz Tony Schultz Tony received his Ph.D. in Physics from the City University of New York & has taught at Sarah Lawrence College over the past decade. Tony specializes in developing machine learning & pattern recognition algorithms for processing motion capture data.

We discuss Tanja's keynote, "Vision or Reality? We discuss the 2 key themes of BCW 2020 – addressing Global Warming / Sustainability and the intersection of AI and IoT, creating AIoT. In addition, Dirk discusses the launch of Bosch's AI Code of Ethics – a set of guidelines that govern how Bosch will use AI with its intelligent products. We discuss the recent rebranding of Bosch Software Innovations to Bosch.IO. What is it and what has changed?

Ethics of AI: While artificial intelligence promises significant benefits, there are concerns it could make unethical decisions. Prefer to listen to this story? Here it is in audio format. Artificial intelligence (AI) is fast becoming important for accountants and businesses, and how it is used raises several ethical issues and questions. While autonomous AI algorithms teach themselves, concerns have been raised that some machine learning techniques are essentially "black boxes" that make it technically impossible to fully understand how the machine arrived at a result. It will become increasingly important to develop AI algorithms that are transparent to inspection, auditable, secure and robust against manipulation and misuse.

"Listening to the data is important… but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model?" One of the most important steps as Data Science is a quantitative domain and core mathematical foundations will serve as a base for your learning. Probability is the measure of the likelihood that an event will occur. A lot of data science is based on attempting to measure the likelihood of events, everything from the odds of an advertisement getting clicked on, to the probability of failure for a part on an assembly line.

Automated analysis of Chest X-ray images to diagnose various pathologies will help in overcoming the costly, time consuming and prone to error from manual analysis of them, especially using deep learning based approaches. One of such recent efforts in this direction is Classification of Common Thorax which combines the advantages of CNN based feature extraction and problem transformation methods in multi-label classification task. So this is one of the key areas where deep learning based solution has already made an impact and has the potential to come up with even a better and well improved performance. For this session, I am going to discuss about the problem at hand, the data-set, several approaches that has been explored and that worked quite well so far in this research. Also I am going to mention about the potential use case and the real world impact of such a real world healthcare application that can save millions of lives by early and effective detection.

Automated analysis of CT scan images using AI solutions to diagnose abnormalities will help in overcoming the costly, time consuming and prone to error from manual analysis. Deep Learning has proved to be quite efficient to mimic human cognitive abilities (and even exceed that in many cases), especially with unstructured data. DL algorithms can detect, localize and quantify a growing list of brain pathologies including intra-cerebral bleeds and their subtypes, infarcts, mass effect, midline shift, and cranial fractures. So, with advanced DL algorithms, analysis of radiographic data can be easily achieved and this can accelerate early detection of certain critical medical conditions, powered by AI. As mentioned, Deep Learning algorithms for computer vision use cases has been extremely successful for classification and localization related problems.

We are looking for deep technical topics related to Data Science. We are committed to diversity and believe in transparency. Hence, all proposals will be public. Registered user will be able to comment on your proposal. You are required to reply to those comments to provide clarifications, explain revisions and respond to questions.

As of March 10, 2020, 467 confirmed cases of COVID-19 have been reported to the Centers for Disease Control and Prevention in the United States. While governments across the globe are working in collaboration with local authorities and health-care providers to track, respond to and prevent the spread of disease caused by the coronavirus, health experts are turning to advanced analytics and artificial intelligence to augment current efforts to prevent further infection. Data and analytics have proved to be useful in combating the spread of disease, and the federal government has access to ample data on the U.S. population's health and travel as well as the migration of both domestic and wild animals -- all of which can be useful in tracking and predicting disease trajectory. Machine learning's ability to consider large amounts of data and offer insights can lead to deeper knowledge about diseases and enable U.S. health and government officials to make better decisions throughout the entire evolution of an outbreak. As the global human population grows and continues to interact with animals, other opportunities for viruses that originate in animals (like COVID-19) could make the jump from to humans and spread.

Campus applications of artificial intelligence may seem to be far in the future. Colleges across the country are experimenting with AI in a variety of ways. Their successes are encouraging, as is the fact that AI is limited only by our imagination. Emerging technologies invite all of us to be active influencers and participants in their evolution. Recently, I heard a colleague discuss technology innovations as a solution to a pervasive problem in higher education: We too often turn senior faculty and administrators into well-paid typists.

The COVID-19 pandemic accelerates an automated future that's already on its way. It serves as a wake-up call to all AI, robotics, and driverless car startups: stop building eye-dazzling demos and talking about the future possibility of general-use AI. Instead, focus on deploying real-world solutions that can run 24 hours a day with minimum human intervention and deliver true value to users. Thousands of Americans have started to work from home amidst the current pandemic. Retailers have struggled with supply while nervous consumers are hoarding everything from toilet paper to hand soap.