For a businessman who perpetually struggles with broken promises, Elon Musk has given himself quite a to-do list at Tesla's long-awaited Hollywood unveiling of its driverless robotaxis. After traversing the fake streets of the Warner Bros movie studio set in a sleek, silver two-door "Cybercab" prototype, Musk promised on Thursday night that the company's popular Model 3 and Model Y vehicles would be able to operate without driver supervision in California and Texas by next year. Musk said the company would start building the fully autonomous Cybercab by 2026 at a price of less than 30,000, and showed off a robovan capable of transporting 20 people around town – which he said would reshape cities by "turning parking lots into parks". Later came the dancing humanoid robots that also mixed drinks at the bar, which Musk said Tesla will also eventually sell for 20,000 to 30,000 each. "I think this will be the biggest product ever, of any kind," he declared.

Founded in 1906, EDHEC is now one of Europe's top 15 business schools . Based in Lille, Nice, Paris, London and Singapore, and counting over 90 nationalities on its campuses, EDHEC is a fully international school directly connected to the business world. With over 40,000 graduates in 120 countries, it trains committed managers capable of dealing with the challenges of a fast-evolving world. Harnessing its core values of excellence, innovation and entrepreneurial spirit, EDHEC has developed a strategic model founded on research of true practical use to society, businesses and students, and which is particularly evident in the work of EDHEC-Risk Institute and Scientific Beta. The School functions as a genuine laboratory of ideas and plays a pioneering role in the field of digital education via EDHEC Online, the first fully online degree-level training platform.

The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way.

This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions. The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques.

We are looking for an exceptional and ambitious Senior Data Scientist to help our team of world class engineers, data scientists and commercial executives develop our Causal AI platform. You'll wear many hats, serving as the domain and data science expert for our solutions in Investment Management. We offer an intellectually stimulating environment, working in an interdisciplinary team and an inclusive culture. We are a high-calibre, mission-driven team building technology that improves our world. This is an exciting role for a smart, creative person with a strong technical background and desire to apply his/her skills in a stimulating scientific environment.

About this Course 18,922 recent views This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions. The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques.

Real estate is not a new industry. It has been formed over the centuries to help make the risky and important process of buying, selling, and leasing properties safer and easier. Many technologies have been adopted to help property professionals make their jobs more efficient. Websites, email, spreadsheets, CRMs, valuation calculators, search engines, all of these have helped change the way real estate is transacted. But it seems like we are approaching the limit on how technology can help humans do their job more efficiently.

The AI Pioneers in Investment Management report from CFA Institute explores global best practices in the application of artificial intelligence (AI) and big data technology in the investment process. Since its launch last year, the report has inspired various compelling inquiries from readers and event participants that are worth addressing. Below are some of the frequently asked questions (FAQs) along with my responses. Please continue to send us your queries and comments by email or in the comments section below, and I will be sure to share and answer those that could benefit the wider audience. We believe an organization's competencies in investments and technology are complementary rather than competing.

In this report, we seek to identify high-impact applications of artificial intelligence (AI) and big data in investments and best practice in their implementation by examining specific use cases. For this purpose, we conducted interviews with investment industry practitioners around the world and from different areas of investments, mostly in April and May 2019. We found that relatively few investment professionals are currently exploiting AI and big data applications in their investment processes. To provide a guidepost for investment firms and individuals seeking to move toward the latest technological frontier, we spoke with a selection of institutions across the globe that are currently using these technologies; these are among the AI pioneers in investment management. Their use cases are illuminating.

Larry Cao, CFA, is the author of AI Pioneers in Investment Management from CFA Institute. AlphaGo brought artificial intelligence (AI) out of computer labs and into the living room. From October 2015, when the AlphaGo AI first beat a professional human competitor, to January 2018, several months after it defeated Ke Jie, the top-ranked player in the world, AI's popularity had tripled as measured by Google Trends. Investment professionals have watched all this from the sidelines with a mixture of excitement and anxiety: Will AI beat humans in investing too? Let me break down some of the report's major revelations.